Imports: methods, stats, graphics, robustbase

Imports: methods, stats, utils, graphics, numDeriv, Rfast, splines,

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 and random-cumpled binomial regression models as alternative to the binomial regression models under the presence of overdispersion; (5) Zero-inflated and zero-altered regression models to deal with zero-excess in count data;(6) generalized estimating equations for cluster correlated data.
Packaged: 2022-09-30 12:42:28 UTC; Luis Hernando

Title: Transfer Learning with Regularized Generalized Linear Models
Authors@R: c(person("Ye", "Tian", role = c("aut", "cre"), email = "ye.t@columbia.edu"), person("Yang", "Feng", role = "aut", email = "yang.feng@nyu.edu"))
Description: We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is available. We also provides visualization for the transferable source detection results. A relevant paper
by Ye Tian and Yang Feng (2021) will be available soon on arXiv.
Imports: glmnet, ggplot2, foreach, doParallel, caret, assertthat,

Title: Transfer Learning under Regularized Generalized Linear Models
Description: We provide an efficient implementation for two-step multi-source transfer learning algorithms in high-dimensional generalized linear models (GLMs). The elastic-net penalized GLM with three popular families, including linear, logistic and Poisson regression models, can be fitted. To avoid negative transfer, a transferable source detection algorithm is proposed. We also provides visualization for the transferable source detection results. The relevant paper
is available on arXiv: <arXiv:2105.14328>.