cgr not working when BIC don't change for given lambda values

[mattocci27]

When BIC (or AIC) don't change for given lambda values (like below),

r$> sparse_fits$BIC
 [1] 221.1714 202.8500 202.8500 202.8500 202.8500 202.8500 202.8500 202.8500 202.8500 202.8500 202.8500

ecoCopula/R/cgr.R

Lines 101 to 108 in 277ab69

	#then find the sweet spot by removing the 5 largest of the 10 values above
	if(method=="BIC"){
	sub_lam=lambda[sparse_fits$BIC<kth_largest(sparse_fits$BIC,5)]
	}else if (method=="AIC"){
	sub_lam=lambda[sparse_fits$AIC<kth_largest(sparse_fits$AIC,5)]
	}else{
	stop("lambda selection method can only be \"AIC\" or \"BIC\" ")
	}

the above lines do not return sub_lam (i.e., sub_lam will be numeric(0)), and subsequent codes will not work properly. This happened some of my datasets.

Currently, I put large lambda values and make completely sparse matrices (identity matrix) for those bad estimates. Because most of the case, the target matrix become completely sparse at the beginning of changing lambda.
https://github.com/mattocci27/ecoCopula/blob/4439a2f1575bcd486b641a9562780a9445932bee/R/cgr.R#L113-L116

I would be willing to start on a PR if maintainers support the idea. I would also appreciate if you could implement a more efficient way to search lambda values.

[gordy2x]

Thanks, I will look at it when I come back to work in mid Jan.