c(
person(given="David", family="Cortes", role=c("aut", "cre", "cph"),
email="david.cortes.rivera@gmail.com"),
person(given="Jean-Sebastien", family="Roy", role="cph",
comment="Copyright holder of included tnc library"),
person(given="Stephen", family="Nash", role="cph",
comment="Copyright holder of included tnc library")
)
David Cortes [aut, cre, cph],
Jean-Sebastien Roy [cph] (Copyright holder of included tnc library),
Stephen Nash [cph] (Copyright holder of included tnc library)

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.

Simulating Homogenous & Non-Homogenous Poisson Processes
Kristian Brock [aut] <kristian.brock@gmail.com>, Daniel Slade [ctb]
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.

Fit high dimensional mixtures of Poisson GLM's
Panagiotis Papastamoulis, Marie-Laure Martin-Magniette, Cathy
Maugis-Rabusseau
High dimensional mixtures of Poisson Generalized Linear
models with three different parameterizations of Poisson means
are considered. Moreover, partitioning the response variables
into a set of blocks is possible. The package estimates
parameters via EM algorithm. For an efficient initialization, a
random splitting small-EM is introduced.

Fit high dimensional mixtures of Poisson GLMs

Panagiotis Papastamoulis, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.