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jaredhuling
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
Hi all, Thanks for the excellent package! Is there any way to use the Area Under the Precision-Recall Curve (AUPRC) as the measure to evaluate cross-validation? This would be useful...
Dear Jared, I coded the Area Under the Precision-Recall Curves (AUPRC) as a measure to evaluate cross-validation. I ran several checks and it is working correctly for me. The only...
I noticed that observation weights are not yet supported - are these not simple to incorporate by noticing that weighted least squares minimizes sum( w * (Y - X %*%...
Hi, in the vignette from 4/2018 (https://jaredhuling.org/oem/articles/oem_vignette.html#available-model-families) it is mentioned that there are plans to support Cox ph. Is that expected to be supported soon? Thank you!
Hello, I am trying to use OEM for grouped LASSO on a tall dataset which contains many categorical variables (and as a result, lots of binary variables in the model...
I tryed using the oem package for variable selection for a quite large dataset, about 1 million observations and several variabales, around 60, both numerical and categorical. I used the...
Would you see any possibility to support box constraints on the fitted coefficients, similar to what can be achieved with lower.limits and upper.limits in glmnet?
self-explanatory, however for some reason the last time I tried this some strange errors came up
Doing this in an efficient way (ie minimal extra copies, clean code workflow) while allowing for matrices not to be group-wise orthonormalized for non-group penalties may be very tricky
This issue is for an implementation of the fast cross validation algorithm as implemented in `xval.oem()` for `big.matrix` objects. This may take some serious work.
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Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm