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adelie (version 1.0.7)

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

1.0.7

License

MIT + file LICENSE

Issues

4

Pull Requests

0

Stars

7

Forks

0

Maintainer

Trevor Hastie

Last Published

February 28th, 2025

Functions in adelie (1.0.7)

glm.binomial

Creates a Binomial GLM family object.
cv.glintnet

Cross-validation for glintnet
glm.poisson

Creates a Poisson GLM family object.
grpnet

fit a GLM with group lasso or group elastic-net regularization
matrix.interaction

Creates a matrix with pairwise interactions.
matrix.eager_cov

Creates an eager covariance matrix.
matrix.dense

Creates a dense matrix object.
matrix.convex_relu

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

Creates a block-diagonal matrix.
io.snp_unphased

IO handler for SNP unphased matrix.
io.snp_phased_ancestry

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

Creates a standardized matrix.
matrix.snp_unphased

Creates a SNP unphased matrix.
matrix.sparse

Creates a sparse matrix object.
plot.cv.glintnet

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

plot coefficients from a "grpnet" object
print.grpnet

print a grpnet object
matrix.lazy_cov

Creates a lazy covariance matrix.
matrix.kronecker_eye

Creates a Kronecker product with an identity matrix.
print.glintnet

Print a summary of the glintnet path at each step along the path.
predict.glintnet

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

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

Creates a concatenation of the matrices.
matrix.one_hot

Creates a one-hot encoded matrix.
predict.grpnet

make predictions from a "grpnet" object.
print.cv.grpnet

print a cross-validated grpnet object
matrix.snp_phased_ancestry

Creates a SNP phased, ancestry matrix.
predict.cv.glintnet

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

make predictions from a "cv.grpnet" object.
set_configs

Set configuration settings.
glm.gaussian

Creates a Gaussian GLM family object.
gaussian_cov

Solves group elastic net via covariance method.
glm.cox

Creates a Cox GLM family object.
cv.grpnet

Cross-validation for grpnet
glintnet

fit a GLM interaction model with group lasso or group elastic-net regularization
glm.multinomial

Creates a Multinomial GLM family object.
glm.multigaussian

Creates a MultiGaussian GLM family object.
constraint.box

Create a box constraint for a group.