Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), RcppEigen

person("Helen", "Ogden", , "heogden12@gmail.com", role = c("aut", "cre"))
Conduct inference about generalized linear mixed models, with a
choice about which method to use to approximate the likelihood. In addition
to the Laplace and adaptive Gaussian quadrature approximations, which are
borrowed from 'lme4', the likelihood may be approximated by the sequential
reduction approximation, or an importance sampling approximation. These
methods provide an accurate approximation to the likelihood in some
situations where it is not possible to use adaptive Gaussian quadrature.
lme4 (>= 1.1-8), Matrix, R6, Rcpp, methods, stats, utils
BradleyTerry2, hglm.data, knitr, rmarkdown, testthat

lme4 (>= 1.1-8), Matrix, R6, Rcpp, methods, stats, utils,
numDeriv

BradleyTerry2, knitr, mdhglm, rmarkdown, testthat

Lasso and elastic-net regularized generalized linear models
Jerome Friedman, Trevor Hastie, Rob Tibshirani
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper on the maintainer's website.

Extremely efficient procedures for fitting the entire
lasso or elastic-net regularization path for linear regression,
logistic and multinomial regression models. The algorithm uses
cyclical coordinate descent in a pathwise fashion, as described
in the paper on the maintainer's website.

Extremely efficient procedures for fitting the entire
lasso or elastic-net regularization path for linear regression,
logistic and multinomial regression models, poisson regression
and the Cox model. The algorithm uses cyclical coordinate
descent in a pathwise fashion, as described in the paper listed
below.

Extremely efficient procedures for fitting the entire
lasso or elastic-net regularization path for linear regression,
logistic and multinomial regression models, poisson regression
and the Cox model. Two recent additions are the multiresponse
gaussian, and the grouped multinomial. The algorithm uses
cyclical coordinate descent in a pathwise fashion, as described
in the paper listed below.