person("Evan", "Wright", email = "enwright@umich.edu",
role = c("aut", "cre"))
Computation of robust standard errors of Poisson fixed effects
models, following Wooldridge (1999).

A Faster Implementation of the Poisson-Binomial Distribution
Santiago Olivella [aut, cre],
Yuki Shiraito [aut, cre]
Provides the probability, distribution, and quantile functions and random number generator for the Poisson-Binomial distribution.  This package relies on FFTW to implement the discrete Fourier transform, so that it is much faster than the existing implementation of the same algorithm in R.

Factorization of Sparse Counts Matrices Through Poisson
Likelihood
Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization
with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, David, 2018, <arXiv:1811.01908>).
Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a hierarchical
structure, and is fit through either proximal gradient or conjugate gradient instead of variational inference.
Rcpp (>= 0.12.19), Matrix, SparseM, methods, nonneg.cg

Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization
with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, (2018) <arXiv:1811.01908>).
Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a
hierarchical structure, and is fit through either proximal gradient or conjugate gradient instead of
variational inference.

David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]
Creates a low-rank factorization of a sparse counts matrix by maximizing Poisson likelihood with l1/l2 regularization
with all non-negative latent factors (e.g. for recommender systems or topic modeling) (Cortes, (2018) <arXiv:1811.01908>).
Similar to hierarchical Poisson factorization, but follows an optimization-based approach with regularization instead of a
hierarchical structure, and is fit through gradient-based methods instead of variational inference.