Package: L2E 2.0

L2E: Robust Structured Regression via the L2 Criterion

An implementation of a computational framework for performing robust structured regression with the L2 criterion from Chi and Chi (2021+). Improvements using the majorization-minimization (MM) principle from Liu, Chi, and Lange (2022+) added in Version 2.0.

Authors:Xiaoqian Liu [aut, ctb], Jocelyn Chi [aut, cre], Lisa Lin [ctb], Kenneth Lange [aut], Eric Chi [aut]

L2E_2.0.tar.gz
L2E_2.0.zip(r-4.7)L2E_2.0.zip(r-4.6)L2E_2.0.zip(r-4.5)
L2E_2.0.tgz(r-4.6-any)L2E_2.0.tgz(r-4.5-any)
L2E_2.0.tar.gz(r-4.7-any)L2E_2.0.tar.gz(r-4.6-any)
L2E_2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
L2E/json (API)

# Install 'L2E' in R:

install.packages('L2E', repos = c('https://jocelynchi.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

bank - Bank data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 3 scripts 233 downloads 20 exports 18 dependencies

Last updated from:a30f7a5396. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	154
source / vignettes	OK	200
linux-release-x86_64	WARNING	151
macos-release-arm64	WARNING	126
macos-oldrel-arm64	WARNING	114
windows-devel	WARNING	101
windows-release	WARNING	108
windows-oldrel	WARNING	101
wasm-release	OK	106

Exports:CV_L2E_sparse_dist CV_L2E_sparse_ncv CV_L2E_TF_dist CV_L2E_TF_lasso L2E_convex L2E_isotonic L2E_multivariate l2e_regression l2e_regression_convex l2e_regression_convex_MM l2e_regression_isotonic l2e_regression_isotonic_MM l2e_regression_MM L2E_sparse_dist L2E_sparse_ncv L2E_TF_dist L2E_TF_lasso myGetDkn objective objective_tau

Dependencies:cli cobs DEoptimR isotone lattice MASS Matrix MatrixModels ncvreg nnls osqp quantreg Rcpp robustbase S7 signal SparseM survival

Introduction to the L2E Package (version 2.0)

Rendered froml2e-intro.Rmdusingknitr::rmarkdown

Last update: 2022-09-08
Started: 2022-01-07

Citation

Readme and manuals

Help Manual

Help page	Topics
Bank data	bank
Cross validation for L2E sparse regression with distance penalization	CV_L2E_sparse_dist
Cross validation for L2E sparse regression with existing penalization methods	CV_L2E_sparse_ncv
Cross validation for L2E trend filtering regression with distance penalization	CV_L2E_TF_dist
Cross validation for L2E trend filtering regression with Lasso penalization	CV_L2E_TF_lasso
L2E	L2E
L2E convex regression	L2E_convex
L2E isotonic regression	L2E_isotonic
L2E multivariate regression	L2E_multivariate
L2E multivariate regression - PG	l2e_regression
L2E convex regression - PG	l2e_regression_convex
L2E convex regression - MM	l2e_regression_convex_MM
L2E isotonic regression - PG	l2e_regression_isotonic
L2E isotonic regression - MM	l2e_regression_isotonic_MM
L2E multivariate regression - MM	l2e_regression_MM
L2E sparse regression with distance penalization	l2e_regression_sparse_dist
L2E sparse regression with existing penalization methods	l2e_regression_sparse_ncv
L2E trend filtering regression with distance penalization	l2e_regression_TF_dist
L2E trend filtering regression with Lasso penalization	l2e_regression_TF_lasso
Solution path of L2E sparse regression with distance penalization	L2E_sparse_dist
Solution path of L2E sparse regression with existing penalization methods	L2E_sparse_ncv
Solution path of the L2E trend filtering regression with distance penalization	L2E_TF_dist
Solution path of the L2E trend filtering regression with Lasso	L2E_TF_lasso
Compute kth order differencing matrix	myGetDkn
Objective function of the L2E regression - eta	objective
Objective function of the L2E regression - tau	objective_tau
Beta update in L2E convex regression - PG	update_beta_convex
Beta update in L2E isotonic regression - PG	update_beta_isotonic
Beta update in L2E convex regression - MM	update_beta_MM_convex
Beta update in L2E isotonic regression - MM	update_beta_MM_isotonic
Beta update in L2E multivariate regression - MM	update_beta_MM_ls
Beta update in L2E sparse regression - MM	update_beta_MM_sparse
Beta update in L2E trend filtering regression - MM	update_beta_MM_TF
Beta update in L2E multivariate regression - PG	update_beta_qr
Beta update in L2E sparse regression - NCV	update_beta_sparse_ncv
Beta update in L2E trend filtering regression using Lasso	update_beta_TF_lasso
Eta update using Newton's method with backtracking	update_eta_bktk
Tau update function	update_tau_R

Package: L2E 2.0

L2E: Robust Structured Regression via the L2 Criterion

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)