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adelie (version 1.0.8)

Group Lasso and Elastic Net Solver for Generalized Linear Models

Description

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) .

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Install

install.packages('adelie')

Monthly Downloads

280

Version

1.0.8

License

MIT + file LICENSE

Issues

4

Pull Requests

0

Stars

7

Forks

0

Maintainer

Trevor Hastie

Last Published

July 1st, 2025

Functions in adelie (1.0.8)

matrix.interaction

Creates a matrix with pairwise interactions.
grpnet

fit a GLM with group lasso or group elastic-net regularization
glm.poisson

Creates a Poisson GLM family object.
io.snp_unphased

IO handler for SNP unphased matrix.
matrix.block_diag

Creates a block-diagonal matrix.
matrix.concatenate

Creates a concatenation of the matrices.
matrix.snp_unphased

Creates a SNP unphased matrix.
matrix.kronecker_eye

Creates a Kronecker product with an identity matrix.
matrix.standardize

Creates a standardized matrix.
matrix.subset

Creates a subset of the matrix along an axis.
matrix.sparse

Creates a sparse matrix object.
predict.grpnet

make predictions from a "grpnet" object.
predict.glintnet

make predictions from a "glintnet" object.
matrix.one_hot

Creates a one-hot encoded matrix.
matrix.snp_phased_ancestry

Creates a SNP phased, ancestry matrix.
matrix.lazy_cov

Creates a lazy covariance matrix.
predict.cv.glintnet

make predictions from a "cv.glintnet" object.
predict.cv.grpnet

make predictions from a "cv.grpnet" object.
matrix.dense

Creates a dense matrix object.
print.grpnet

print a grpnet object
set_configs

Set configuration settings.
print.glintnet

Print a summary of the glintnet path at each step along the path.
print.cv.grpnet

print a cross-validated grpnet object
plot.cv.glintnet

plot the cross-validation curve produced by cv.glintnet
plot.grpnet

plot coefficients from a "grpnet" object
glintnet

fit a GLM interaction model with group lasso or group elastic-net regularization
gaussian_cov

Solves group elastic net via covariance method.
glm.cox

Creates a Cox GLM family object.
glm.gaussian

Creates a Gaussian GLM family object.
glm.binomial

Creates a Binomial GLM family object.
cv.grpnet

Cross-validation for grpnet
constraint.box

Create a box constraint for a group.
glm.multinomial

Creates a Multinomial GLM family object.
cv.glintnet

Cross-validation for glintnet
glm.multigaussian

Creates a MultiGaussian GLM family object.
io.snp_phased_ancestry

IO handler for SNP phased, ancestry matrix.
matrix.convex_relu

Creates a feature matrix for the convex relu problem.
matrix.eager_cov

Creates an eager covariance matrix.