Suggests: covr, ggplot2, knitr, logisticPCA, markdown, Matrix,

Title: Inference in Generalized Linear Models
Author: Wiebke Werft and Douglas M. Potter
Description: 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

Author: Wiebke Werft, Axel Benner and Douglas M. Potter

Title: Truncated Lasso Regularized Generalized Linear Models
Description: 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>).
Packaged: 2018-02-07 03:25:58 UTC; uniquechong

Title: Generalized Linear Models with Truncated Lasso Penalty
person("Yu", "Yang", email = "yang6367@umn.edu", role = c("aut", "cre")),
Depends: R (>= 3.5.0), foreach, parallel
Description: 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.

Title: Set of Tools to Data Analysis using Generalized Linear Models
Description: 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.
Author: Luis Hernando Vanegas [aut, cre],
Maintainer: Luis Hernando Vanegas <lhvanegasp@unal.edu.co>