methods, stats, utils, graphics, numDeriv, Rfast, splines,
Formula

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.

Transfer Learning with Regularized Generalized Linear Models
c(person("Ye", "Tian", role = c("aut", "cre"), email = "ye.t@columbia.edu"), person("Yang", "Feng", role = "aut", email = "yang.feng@nyu.edu"))
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.
glmnet, ggplot2, foreach, doParallel, caret, assertthat,
formatR, stats

Transfer Learning under Regularized Generalized Linear Models
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>.

c(person("Adrien", "Ehrhardt", email = "adrien.ehrhardt@centraliens-lille.org",
role = c("aut", "cre")))
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user's criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://github.com/adimajo/manuscrit_these/releases/>.
partykit, magrittr, methods, dplyr, caret
FactoMineR, knitr, testthat, covr, rmarkdown