Package {RoBMA}

a brma model object.

ignored.

ignored; retained for the stats::AIC() method signature.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

prior distribution(s) for the null cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of the number of events in the treatment or experimental group for binomial GLMM models.

a vector of the number of non-events in the treatment or experimental group for binomial GLMM models.

a vector of the number of events in the control group for binomial GLMM models.

a vector of the number of non-events in the control group for binomial GLMM models.

a vector of the sample size in the treatment or experimental group. If omitted for binomial GLMMs, it is computed as ai + bi.

a vector of the sample size in the control group. If omitted for binomial GLMMs, it is computed as ci + di.

a vector of the number of events in the treatment/experimental group (for Poisson data).

a vector of the number of events in the control group (for Poisson data).

a vector of the person-time in the treatment/experimental group.

a vector of the person-time in the control group.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution for the estimate-specific midpoint base-rate probability in binomial GLMM models (measure = "OR"). If omitted or NULL, defaults to independent Beta(1, 1) priors. Continuous priors are restricted to ⁠[0, 1]⁠ (see prior_specification) and point priors must lie strictly inside ⁠(0, 1)⁠. The estimate-unit log-likelihood of the model (log_lik(), LOO, WAIC) integrates the base rate over any of these priors: point priors and untruncated beta priors adaptively (a uniform(0, 1) prior is a Beta(1, 1) prior), other families on a refined prior-CDF grid. The grid is accepted only when its log-likelihood changes by less than 1e-6 between refinements; any prior without an adaptive route (other than an untruncated beta prior; a truncated beta prior takes the grid too) can fail to reach this, especially with few events or very large arms, and then stops with an error of class RoBMA_glmm_grid_nonconvergence, as a lognormal base-rate prior does on cells with few events. A beta prior avoids it. The automatic computation of LOO/WAIC after fitting warns instead and returns the fit without them.

prior distribution for the estimate-specific midpoint log-rate in Poisson GLMM models (measure = "IRR"). If omitted or NULL, a normal prior centered at the pooled crude log-rate is used independently for each estimate. Its standard deviation defaults to 1 and can be changed via RoBMA.options(default_lograte.sd = ...). Rescaling all person-times shifts the prior mean by the corresponding log conversion while leaving its standard deviation unchanged. The estimate-unit log-likelihood of the model (log_lik(), LOO, WAIC) integrates the log-rate over the prior: point priors and untruncated normal priors adaptively, other families on a refined prior-CDF grid. The grid is accepted only when its log-likelihood changes by less than 1e-6 between refinements; any prior without an adaptive route (other than an untruncated normal prior; a truncated normal prior takes the grid too) can fail to reach this, especially with few events or very large arms, and then stops with an error of class RoBMA_glmm_grid_nonconvergence, as gamma, lognormal, and inverse-gamma log-rate priors do on cells with few events. A normal prior avoids it. The automatic computation of LOO/WAIC after fitting warns instead and returns the fit without them.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

prior distribution(s) for the null cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.

optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.

scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent ⁠D + BB'⁠ decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution(s) for alternative publication-bias component(s), such as weight functions, PET, or PEESE.

Alternative prior arguments can be:

• A single prior distribution object (creates a mixture with one alternative)

• A list of prior distributions (creates a mixture with multiple alternatives)

• NULL or FALSE (omits the alternative hypothesis component)

See publication_bias_prior_specification for details on specifying publication-bias priors and prior_specification for details on specifying meta-analytic parameter priors.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

prior distribution(s) for the null cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution(s) for null publication-bias component(s), usually prior_none(). See publication_bias_prior_specification.

Null prior arguments can be:

• A single prior distribution object (creates a mixture with one null)

• A list of prior distributions (creates a mixture with multiple nulls)

• NULL or FALSE (omits the null hypothesis component)

Defaults to a point mass (spike) at zero for effect and heterogeneity parameters.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

character string specifying predefined publication-bias model ensembles. One of:

• "PSMA" (default): Full RoBMA-PSMA ensemble with 6 weight functions + PET + PEESE

• "6w": Six weight function models

• "2w": Two weight function models

• "PP": PET-PEESE models only

Custom prior_bias replaces the preset alternative bias components. If prior_bias is omitted, model_type determines the default alternatives even when prior_bias_null is customized or omitted.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

specification created by selection_model() for automatically constructed weightfunction priors. The default integrates estimate-level random effects and the complete sampling error, and conditions on other random effects. Each source can instead be conditioned upon or integrated through the corresponding selection-model setting. The default weight_rule = "product" needs no publication groups and ignores group. Publication grouping is active only for weight_rule = "best". Explicit prior_bias objects retain their own selection specification; this argument does not overwrite it. Conditioning on sampling variation retains the entire sampling-error realization, including in ordinary univariate models. This sampling setting requires omitted or unit observation weights.

numerical integration settings created by set_selection_likelihood_control(). These affect numerical evaluation, without changing the conditioning model or the vector weighting rule.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.

optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.

scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for alternative publication-bias component(s), such as weight functions, PET, or PEESE.

Alternative prior arguments can be:

• A single prior distribution object (creates a mixture with one alternative)

• A list of prior distributions (creates a mixture with multiple alternatives)

• NULL or FALSE (omits the alternative hypothesis component)

See publication_bias_prior_specification for details on specifying publication-bias priors and prior_specification for details on specifying meta-analytic parameter priors.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

prior distribution(s) for null publication-bias component(s), usually prior_none(). See publication_bias_prior_specification.

Null prior arguments can be:

• A single prior distribution object (creates a mixture with one null)

• A list of prior distributions (creates a mixture with multiple nulls)

• NULL or FALSE (omits the null hypothesis component)

Defaults to a point mass (spike) at zero for effect and heterogeneity parameters.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

character string specifying predefined publication-bias model ensembles. One of:

• "PSMA" (default): Full RoBMA-PSMA ensemble with 6 weight functions + PET + PEESE

• "6w": Six weight function models

• "2w": Two weight function models

• "PP": PET-PEESE models only

Custom prior_bias replaces the preset alternative bias components. If prior_bias is omitted, model_type determines the default alternatives even when prior_bias_null is customized or omitted.

specification created by selection_model() for automatically constructed weightfunction priors. The default integrates estimate-level random effects and the complete sampling error, and conditions on other random effects. Each source can instead be conditioned upon or integrated through the corresponding selection-model setting. The default weight_rule = "product" needs no publication groups and ignores group. Publication grouping is active only for weight_rule = "best". Explicit prior_bias objects retain their own selection specification; this argument does not overwrite it. Conditioning on sampling variation retains the entire sampling-error realization, including in ordinary univariate models. This sampling setting requires omitted or unit observation weights.

numerical integration settings created by set_selection_likelihood_control(). These affect numerical evaluation, without changing the conditioning model or the vector weighting rule.

known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent ⁠D + BB'⁠ decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

maximum value of the R-hat diagnostic. Defaults to 1.05.

minimum estimated sample size. Defaults to 500.

maximum value of the MCMC error. Defaults to NULL. Be aware that PEESE publication bias adjustment can have estimates on different scale than the rest of the output, resulting in relatively large max MCMC error.

maximum value of the proportion of MCMC error of the estimated SD of the parameter. Defaults to NULL.

list with the time and unit specifying the maximum autofitting process per model. Passed to difftime function (possible units are "secs", "mins", "hours", "days", and "weeks"). Defaults to list(time = 60, unit = "mins").

number of samples to extend the fitting process if the criteria are not satisfied. Defaults to 1000.

number of times new initial values should be generated in case a model fails to initialize. Defaults to 10.

number of times after which the automatic fitting function is stopped. Defaults to 10.

whether model indicator variables should be included in convergence checks. Binary indicators are checked as state occupancies and categorical indicators are checked separately for every observed state. Eligible indicators remain included when monitor selects a narrower parameter set; auxiliary inclusion-probability coordinates remain excluded unless requested explicitly. Defaults to FALSE.

optional character vector selecting the parameters used for convergence checks. A base name selects all indexed elements. Defaults to NULL, which checks every eligible parameter.

whether requested sampled parameters with undefined diagnostics may be ignored. Defaults to FALSE.

named option(s) to change. Names must be exact public option names, nonempty, and unique.

a single non-missing character string matching one public option exactly; for available options, see details below.

prior distribution(s) for the alternative effect component(s).

prior distribution(s) for the alternative heterogeneity component(s).

prior distribution(s) for alternative moderator components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. For a formula without an intercept (~ 0 + x), an intercept entry other than a point prior at zero stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named alternative intercept must be a single simple or point prior; it is the complete intercept prior and replaces the global effect mixture. Specify it in one place: null-list intercepts and explicit global effect declarations left without a consumer are refused. Ordinary term entries replace only their alternative component.

prior distribution(s) for alternative scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified. A named alternative intercept must be a single positive simple or point prior; it is the complete SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its inclusion weights; declarations replaced without a consumer are refused. Null-list intercept declarations are unavailable.

prior distribution(s) for the alternative cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution(s) for alternative publication-bias component(s), such as weight functions, PET, or PEESE.

Alternative prior arguments can be:

• A single prior distribution object (creates a mixture with one alternative)

• A list of prior distributions (creates a mixture with multiple alternatives)

• NULL or FALSE (omits the alternative hypothesis component)

See publication_bias_prior_specification for details on specifying publication-bias priors and prior_specification for details on specifying meta-analytic parameter priors.

prior distribution(s) for the null effect component(s).

prior distribution(s) for the null heterogeneity component(s).

prior distribution(s) for null moderator components. A named intercept entry, including NULL or FALSE, is unavailable: put the complete intercept prior in prior_mods, or use global prior arguments without a formula-list intercept. The same rule applies to prior_scale_null. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when mods is specified. The null and the alternative prior of a factor term need the same contrast; a conflict stops with an error of class RoBMA_prior_contrast_conflict (also of class RoBMA_prior_error) that names the term and the remedies. The same rule applies to prior_scale_null.

prior distribution(s) for null scale-regression components. A single prior applies to all terms; a named list can specify term-specific components. This argument can be specified only when scale is specified.

prior distribution(s) for the null cluster-level heterogeneity allocation component(s). This argument can be specified only when cluster is specified.

prior distribution(s) for null publication-bias component(s), usually prior_none(). See publication_bias_prior_specification.

Null prior arguments can be:

• A single prior distribution object (creates a mixture with one null)

• A list of prior distributions (creates a mixture with multiple nulls)

• NULL or FALSE (omits the null hypothesis component)

Defaults to a point mass (spike) at zero for effect and heterogeneity parameters.

character string specifying predefined publication-bias model ensembles. One of:

• "PSMA" (default): Full RoBMA-PSMA ensemble with 6 weight functions + PET + PEESE

• "6w": Six weight function models

• "2w": Two weight function models

• "PP": PET-PEESE models only

Custom prior_bias replaces the preset alternative bias components. If prior_bias is omitted, model_type determines the default alternatives even when prior_bias_null is customized or omitted.

a brma model object.

output/deletion unit. "estimate" computes one contribution per effect-size estimate. "cluster" computes one contribution per cluster and is available only for multilevel models fitted through the specialized clustered interface.

optional vector of relative effective sample sizes. If not provided, it is computed from the log-likelihood values.

Logical. If TRUE, loo::relative_eff() and loo::loo() use RoBMA.get_option("max_cores"). Log-likelihood construction is unchanged. If FALSE, those computations use one core.

additional arguments (currently ignored).

a brma model object.

whether bridge-density evaluations run in parallel. The default, NULL, inherits the setting used to fit object.

number of bridge-sampling worker processes. The default, NULL, inherits the fitted core count and is capped by RoBMA.get_option("max_cores"). It is used only when parallel = TRUE.

number of independent bridge-sampling repetitions.

bridge transformation; either "normal" or "warp3".

maximum number of bridge iterations per repetition.

whether bridge-sampling progress is suppressed. The typed iteration-limit diagnostic remains visible even when silent = TRUE.

reserved for future use. Unused arguments produce a warning.

a brma model object.

output/deletion unit. See add_loo; the same accepted values and multilevel constraint apply.

additional arguments passed to waic.

a fitted brma or summary.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

for a fitted object, additional arguments passed to summary.brma(); otherwise unused.

a brma_samples, summary.brma_samples, or multi-component brma_samples_list result

NULL or a character vector giving the row names for the resulting data frame. Custom row names are unsupported when format = "list".

logical; passed to the final data-frame coercion method.

for a brma_samples result, additional arguments passed to the matrix coercion; for a brma_samples_list, additional arguments passed to summary.brma_samples()

logical; passed to the output data frame.

for multi-component results, whether to return a single "long" data frame or a named, possibly nested "list" with a plain data frame at each leaf. Defaults to "long".

a covratio.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

a dfbetas.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

an infl.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

a marginal_means.brma or summary.marginal_means.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

additional arguments passed to summary().

a summary_heterogeneity.brma or summary_heterogeneity.brma_list object.

NULL or a character vector giving the row names for the result. Custom row names are unsupported when format = "list".

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

for multi-component results, whether to return one "long" data frame or a named "list" of data frames.

a summary_models.RoBMA object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

a vif.brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

unused additional arguments.

a zplot_brma or summary.zplot_brma object.

NULL or a character vector giving the row names.

logical; passed to the final data-frame coercion.

accepted for compatibility with data.frame().

additional arguments passed to summary() for a zplot_brma object and otherwise unused.

a brma_samples object

additional arguments (ignored)

an object to convert. The brma methods expect a fitted brma object; default methods forward non-brma objects to the corresponding posterior conversion function.

additional arguments passed to the corresponding posterior function.

logical; whether to include the hidden intermediate parameters (see Details) in addition to the parameters shown by the default print() and summary() of the fit. Formula-random parameters retain their RoBMA semantic names in either case.

a brma_samples object

additional arguments passed to the corresponding posterior function

a fitted brma object.

additional named arguments stored in forest_args and later passed to forest.default. Shared graphical arguments are also stored in addpoly_args. This includes metafor's auxiliary-column arguments ilab, ilab.lab, ilab.xpos, and ilab.pos.

credible/confidence level in percent, greater than 0 and less than 100. A value below 1 is read as a proportion (0.95 is 95 percent), so 1 means 1 percent. Defaults to 95.

logical; whether to compute a marginal posterior prediction interval for one new true effect. Defaults to FALSE.

optional one-row prediction design used when addpred = TRUE. When omitted, the interval is evaluated at the average expanded design using pooled_effect(). When supplied, each row passed to predict.brma() is one new true effect; with bias_adjusted = FALSE, it targets a selected new publication. The pooled reference declares no new-publication SE or selection event. The forest adapter accepts exactly one because it draws one summary polygon.

logical; whether pooled and prediction summaries should adjust for publication bias. Defaults to TRUE.

logical; whether to return conditional posterior summaries for RoBMA product-space objects. Defaults to FALSE.

a normal-outcome brma object.

positive z-value threshold for significance. Defaults to qnorm(0.975) (two-sided alpha = 0.05). The threshold must be finite and its two-sided alpha, 2 * pnorm(significance_level, lower.tail = FALSE), strictly between 0 and 1. Zero (alpha = 1), a threshold so small that its alpha rounds to 1 (below about 1e-16), and one so large that its alpha underflows to 0 (above about 38.4) are refused because the Soric false discovery rate is undefined there.

maximum number of posterior samples for the EDR and missing-study summaries. Defaults to 10000. Use Inf to use all posterior samples. The density budget is supplied separately to plot() or lines() and defaults to 1000 for brma.mv models and 10000 for other models.

predictive conditioning depth. Options are "marginal" (default), which integrates all newly realized random effects; "cluster", which retains fitted cluster effects and integrates within-cluster heterogeneity; and "estimate", which retains each fitted latent true effect, integrates its posterior conditional uncertainty, and adds new sampling error. Cluster conditioning is available only for multilevel models. Selection models currently support only "marginal" conditioning.

numerical integration settings created by set_selection_likelihood_control(). These control integration within each posterior draw, independently of max_samples. Factor blocks and supported full-covariance product-selection blocks use deterministic Gauss-Hermite quadrature where possible, with the existing adaptive randomized quasi-Monte Carlo fallback. Density-error checks use the peak within each posterior draw and also check the selection normalizer. Bounded CDF interpolation, Gaussian-mixture approximation, and omitted contributions are charged to the same numerical error budget.

additional arguments (currently unused).

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

prior distribution for the fraction of heterogeneity allocated to the cluster-level component in multilevel models (\rho, a variance fraction rather than a random-coefficient correlation). If omitted or NULL, defaults to Beta(1, 1). This argument can be specified only when cluster is specified.

PEESE regression-coefficient prior created by prior_PEESE(). If omitted or NULL, the default is a Cauchy prior centered at 0, truncated to positive values, with scale from RoBMA.get_option("default_bias_PEESE.scale") after UISD adjustment.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.

optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.

scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

PEESE regression-coefficient prior created by prior_PEESE(). If omitted or NULL, the default is a Cauchy prior centered at 0, truncated to positive values, with scale from RoBMA.get_option("default_bias_PEESE.scale") after UISD adjustment.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent ⁠D + BB'⁠ decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

prior distribution for the fraction of heterogeneity allocated to the cluster-level component in multilevel models (\rho, a variance fraction rather than a random-coefficient correlation). If omitted or NULL, defaults to Beta(1, 1). This argument can be specified only when cluster is specified.

PET regression-coefficient prior created by prior_PET(). If omitted or NULL, the default is a Cauchy prior centered at 0, truncated to positive values, with scale from RoBMA.get_option("default_bias_PET.scale").

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.

optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.

scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

PET regression-coefficient prior created by prior_PET(). If omitted or NULL, the default is a Cauchy prior centered at 0, truncated to positive values, with scale from RoBMA.get_option("default_bias_PET.scale").

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.

known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent ⁠D + BB'⁠ decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

a brma model object (numerator).

a brma model object (denominator).

logical; if TRUE, the log Bayes factor is returned. Default is FALSE.

additional arguments (currently not used).

a fitted model object

additional arguments passed to methods

a fitted brma object

whether to adjust for publication bias. Defaults to FALSE, which returns estimates including publication bias effects (i.e., what we expect the true effects to be given the biased observations). Set to TRUE to obtain bias-corrected estimates.

effect-size measure for location/effect predictions. Defaults to the fitted measure. Supported conversions are among "SMD", "COR", "ZCOR", and "OR"; "RR", "HR", "IRR", "RD", and "GEN" can only be returned on their fitted measure. Use transform = "EXP" for ratio-scale output from log-scale measures.

optional display transformation. Currently "EXP" exponentiates log-scale measures "OR", "RR", "HR", and "IRR".

quantiles of the posterior distribution to be displayed. Defaults to c(.025, .975) for 95% credible intervals.

additional arguments passed to predict.brma; wrapper arguments such as newdata, type, quiet, output_measure, and transform are fixed by this method (supplying one is R's error for an argument matched twice, as for predict()), and other unknown arguments are unused and trigger a warning.

a brma model object.

additional arguments (currently not used).

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

prior distribution for the fraction of heterogeneity allocated to the cluster-level component in multilevel models (\rho, a variance fraction rather than a random-coefficient correlation). If omitted or NULL, defaults to Beta(1, 1). This argument can be specified only when cluster is specified.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of the number of events in the treatment or experimental group for binomial GLMM models.

a vector of the number of non-events in the treatment or experimental group for binomial GLMM models.

a vector of the number of events in the control group for binomial GLMM models.

a vector of the number of non-events in the control group for binomial GLMM models.

a vector of the sample size in the treatment or experimental group. If omitted for binomial GLMMs, it is computed as ai + bi.

a vector of the sample size in the control group. If omitted for binomial GLMMs, it is computed as ci + di.

a vector of the number of events in the treatment/experimental group (for Poisson data).

a vector of the number of events in the control group (for Poisson data).

a vector of the person-time in the treatment/experimental group.

a vector of the person-time in the control group.

an optional vector of finite positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study. An integer weight w repeats that conditional likelihood contribution w times, with the declared latent-effect and grouping structure held fixed. Positive fractional likelihood powers are also supported.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

an optional vector of cluster identifiers for multilevel meta-analysis.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

prior distribution for the fraction of heterogeneity allocated to the cluster-level component in multilevel models (\rho, a variance fraction rather than a random-coefficient correlation). If omitted or NULL, defaults to Beta(1, 1). This argument can be specified only when cluster is specified.

prior distribution for the estimate-specific midpoint base-rate probability in binomial GLMM models (measure = "OR"). If omitted or NULL, defaults to independent Beta(1, 1) priors. Continuous priors are restricted to ⁠[0, 1]⁠ (see prior_specification) and point priors must lie strictly inside ⁠(0, 1)⁠. The estimate-unit log-likelihood of the model (log_lik(), LOO, WAIC) integrates the base rate over any of these priors: point priors and untruncated beta priors adaptively (a uniform(0, 1) prior is a Beta(1, 1) prior), other families on a refined prior-CDF grid. The grid is accepted only when its log-likelihood changes by less than 1e-6 between refinements; any prior without an adaptive route (other than an untruncated beta prior; a truncated beta prior takes the grid too) can fail to reach this, especially with few events or very large arms, and then stops with an error of class RoBMA_glmm_grid_nonconvergence, as a lognormal base-rate prior does on cells with few events. A beta prior avoids it. The automatic computation of LOO/WAIC after fitting warns instead and returns the fit without them.

prior distribution for the estimate-specific midpoint log-rate in Poisson GLMM models (measure = "IRR"). If omitted or NULL, a normal prior centered at the pooled crude log-rate is used independently for each estimate. Its standard deviation defaults to 1 and can be changed via RoBMA.options(default_lograte.sd = ...). Rescaling all person-times shifts the prior mean by the corresponding log conversion while leaving its standard deviation unchanged. The estimate-unit log-likelihood of the model (log_lik(), LOO, WAIC) integrates the log-rate over the prior: point priors and untruncated normal priors adaptively, other families on a refined prior-CDF grid. The grid is accepted only when its log-likelihood changes by less than 1e-6 between refinements; any prior without an adaptive route (other than an untruncated normal prior; a truncated normal prior takes the grid too) can fail to reach this, especially with few events or very large arms, and then stops with an error of class RoBMA_glmm_grid_nonconvergence, as gamma, lognormal, and inverse-gamma log-rate priors do on cells with few events. A normal prior avoids it. The automatic computation of LOO/WAIC after fitting warns instead and returns the fit without them.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.

a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.

an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.

an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.

an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().

Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.

optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.

scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.

an optional data frame containing the variables.

an optional vector of study labels.

an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.

a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".

prior distribution for the effect size (\mu) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.

prior distribution for the heterogeneity (\tau) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.

prior distribution for the moderators (\beta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.

prior distribution for the scale (\delta) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.

logical. Whether to standardize continuous predictors. Defaults to TRUE.

character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".

numeric. The unit information standard deviation (\sigma_{unit}). Cannot be used together with prior_informed_field.

numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.

character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.

character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.

known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent ⁠D + BB'⁠ decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.

numeric. Number of MCMC samples to save. Defaults to 5000.

numeric. Number of burn-in iterations. Defaults to 2000.

numeric. Number of adaptation iterations. Defaults to 500.

numeric. Number of MCMC chains. Defaults to 3.

numeric. Thinning interval. Defaults to 1.

logical. Whether to run MCMC chains in parallel. Defaults to FALSE.

logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.

list of autofit control settings. See set_autofit_control() for details.

list of convergence check settings. See set_convergence_checks() for details.

numeric. Random seed for reproducibility. Defaults to NULL.

logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.

additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.

a vector of sampling variances. Either vi or sei must be supplied for normal models.

a vector of standard errors. Either vi or sei must be supplied for normal models.