Dimensionality reduction with proDenICA

Hello,

I have a dataset with many variables and I'd like to do dimensionality 
reduction with Independent Component Analysis. There are many 
statistical methods to estimate the latent variables of the ICA model. 
I'm trying the R package "proDenICA" that implements the penalized 
maximum likelihood method proposed by Hastie, Tibshirani and Friedman in 
Section 14.7.4 of the book "Elements of Statistical Learning". The 
documentation of the proDenICA function says that the argument "k" is 
the "Number of components required, less than or equal to the number of 
columns of x". If I choose a value of k less than the number of colomns 
of x, I get an error message. It seems to me that I'm not using the 
function proDenICA() as it is meant to be used. Am I missing something?

I've reproduced the problem with a smaller dataset here:

 > library(MASS)
 > data(crabs)
 > str(crabs)
'data.frame':    200 obs. of  8 variables:
  $ sp   : Factor w/ 2 levels "B","O": 1 1 1 1 1 1 1 1 1 1 ...
  $ sex  : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
  $ index: int  1 2 3 4 5 6 7 8 9 10 ...
  $ FL   : num  8.1 8.8 9.2 9.6 9.8 10.8 11.1 11.6 11.8 11.8 ...
  $ RW   : num  6.7 7.7 7.8 7.9 8 9 9.9 9.1 9.6 10.5 ...
  $ CL   : num  16.1 18.1 19 20.1 20.3 23 23.8 24.5 24.2 25.2 ...
  $ CW   : num  19 20.8 22.4 23.1 23 26.5 27.1 28.4 27.8 29.3 ...
  $ BD   : num  7 7.4 7.7 8.2 8.2 9.8 9.8 10.4 9.7 10.3 ...
 > X=crabs[,4:8]
 > X=as.matrix(X)
 > library(ProDenICA)
 > out.proDen = ProDenICA(X, k = 2, whiten = TRUE, maxit = 20, trace=T)
Error in solve.default(V, W) : 'a' (5 x 2) must be square

I get the error with k = 1,2,3,4. The function works with k=5.

Thank you.