Implements a generalized version of principal components analysis
(GLM-PCA) for dimension reduction of non-normally distributed data such as
counts or binary matrices.
Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1186/s13059-019-1861-6>.
Townes FW (2019) <arXiv:1907.02647>.
covr, ggplot2, knitr, logisticPCA, markdown, Matrix,
testthat,

A permutation test is used for inference in generalized
linear models (glm). The procedure is useful when parameter
estimates in ordinary glm fail to converge or are unreliable
due to small sample size.

Wiebke Werft, Axel Benner and Douglas M. Potter

Truncated Lasso Regularized Generalized Linear Models
It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).

Generalized Linear Models with Truncated Lasso Penalty
c(
person("Chunlin", "Li", role = "aut"),
person("Yu", "Yang", email = "yang6367@umn.edu", role = c("aut", "cre")),
person("Chong", "Wu", role = "aut"))
Extremely efficient procedures for fitting regularization path with l0, l1, and truncated lasso penalty for linear regression and logistic regression models. This version is a completely new version compared with our previous version, which was mainly based on R. New core algorithms are developed and are now written in C++ and highly optimized.
Chunlin Li [aut],
Yu Yang [aut, cre],
Chong Wu [aut]

Set of Tools to Data Analysis using Generalized Linear Models
c(person("Luis Hernando","Vanegas",role=c("aut","cre"),email="lhvanegasp@unal.edu.co"),
person("Luz Marina","Rondón",role="aut",email="lmrondonp@unal.edu.co"),
person("Gilberto A.","Paula",role="aut",email="giapaula@ime.usp.br"))
Set of tools to the statistical analysis of data using: (1) normal linear models; (2) generalized linear models; (3) negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion; (4) beta-binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) generalized estimating equations for cluster correlated data.
Luis Hernando Vanegas [aut, cre],
Luz Marina Rondón [aut],
Gilberto A. Paula [aut]