Estimating Marginal Effects with Prognostic Covariate Adjustment

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Documentation for package ‘postcard’ version 1.0.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

coef.rctglm	Methods for objects of class 'rctglm'
default_learners	Creates a list of learners
est	Methods for objects of class 'rctglm'
estimand	Methods for objects of class 'rctglm'
estimand.rctglm	Methods for objects of class 'rctglm'
fit_best_learner	Find the best learner in terms of RMSE among specified learners using cross validation
glm_data	Generate data simulated from a GLM
options	postcard Options
power_gs	Power and sample size estimation for linear models
power_linear	Power and sample size estimation for linear models
power_marginaleffect	Power approximation for estimating marginal effects in GLMs
power_nc	Power and sample size estimation for linear models
print.rctglm	Methods for objects of class 'rctglm'
prog	Extract information about the fitted prognostic model
prog.rctglm_prog	Extract information about the fitted prognostic model
rctglm	Fit GLM and find any estimand (marginal effect) using plug-in estimation with variance estimation using influence functions
rctglm_methods	Methods for objects of class 'rctglm'
rctglm_with_prognosticscore	Use prognostic covariate adjustment when fitting an rctglm
samplesize_gs	Power and sample size estimation for linear models
variance_ancova	Power and sample size estimation for linear models