Title: A Faster Implementation of the Poisson-Binomial Distribution
Description: 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.

Title: Factorization of Sparse Counts Matrices Through Poisson
Description: 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.
Imports: Rcpp (>= 0.12.19), Matrix, SparseM, methods, nonneg.cg

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

Author: David Cortes [aut, cre, cph], Jean-Sebastien Roy [cph], Stephen Nash [cph]
hierarchical structure, and is fit through gradient-based methods instead of variational inference.

comment="Copyright holder of included tnc library"),
comment="Copyright holder of included tnc library")
Jean-Sebastien Roy [cph] (Copyright holder of included tnc library),
Stephen Nash [cph] (Copyright holder of included tnc library)

Description: Creates a non-negative low-rank approximate factorization of a sparse counts matrix by maximizing Poisson
likelihood with L1/L2 regularization (e.g. for implicit-feedback recommender systems or bag-of-words-based topic modeling)
(Cortes, (2018) <arXiv:1811.01908>), which usually leads to very sparse user and item factors (over 90% zero-valued).
Similar to hierarchical Poisson factorization (HPF), but follows an optimization-based approach with regularization
instead of a hierarchical prior, and is fit through gradient-based methods instead of variational inference.