[R] singular factor analysis

[Spencer Graves]

      Are there any functions available to do a factor analysis with 
fewer observations than variables?  As long as you have more than 3 
observations, my computations suggest you have enough data to estimate a 
factor analysis covariance matrix, even though the sample covariance 
matrix is singular.  I tried the naive thing and got an error: 

 > set.seed(1)
 > X <- array(rnorm(50), dim=c(5, 10))
 > factanal(X, factors=1)
Error in solve.default(cv) : system is computationally singular: 
reciprocal condition number = 4.8982e-018

      I can write a likelihood for a multivariate normal and solve it, 
but I wondered if there is anything else available that could do this? 

      Thanks,
      Spencer Graves