NEWS.md
In glmpca: Dimension Reduction of Non-Normally Distributed Data

News

All notable changes to glmpca will be documented in this file. The format is based on Keep a Changelog

This release should be backwards-compatible even though some arguments are now deprecated.

AvaGrad optimizer which is faster and more numerically stable than Fisher scoring.
Minibatch methods (memoized and stochastic gradients) to support large sparse Matrices
Automatic tuning of optimization hyperparameters (learning rate or penalty)
Negative binomial likelihood with feature-specific overdispersion parameters
print and predict methods for fitted glmpca object

more detailed documentation of main glmpca function
split off large blocks of code in the main glmpca function to separate functions
organize results as an S3 class instead of a list
moved arguments penalty and verbose into the ctl parameters list
deprecated family mult and bern, replaced with family binom
control parameter eps has now been renamed tol

Initial release of the GLM-PCA method of dimension reduction. The primary focus is on count data (Poisson and negative binomial likelihoods). Bernoulli likelihood has also been implemented for binary data but this has not been tested as thoroughly.

basic checking for error conditions such as the data matrix having rows or columns that are all zero
L2 (ridge) penalty on latent variables to improve numerical stability
Arbitrary row and/or column covariates may be included to remove nuisance variation from latent factors.
negative binomial shape parameter automatically estimated.
latent factors automatically projected, scaled, and rotated to remove correlation with covariates and maintain orthogonality and improve interpretability.