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
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L2E.pdf |L2E.html✨
L2E/json (API)

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

Peer review:

Datasets:

On CRAN:

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

20 exports 0.00 score 17 dependencies 2 scripts 252 downloads

Last updated 2 years agofrom:a30f7a5396. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-winWARNINGSep 17 2024
R-4.5-linuxWARNINGSep 17 2024
R-4.4-winWARNINGSep 17 2024
R-4.4-macWARNINGSep 17 2024
R-4.3-winWARNINGSep 17 2024
R-4.3-macWARNINGSep 17 2024

Exports:CV_L2E_sparse_distCV_L2E_sparse_ncvCV_L2E_TF_distCV_L2E_TF_lassoL2E_convexL2E_isotonicL2E_multivariatel2e_regressionl2e_regression_convexl2e_regression_convex_MMl2e_regression_isotonicl2e_regression_isotonic_MMl2e_regression_MML2E_sparse_distL2E_sparse_ncvL2E_TF_distL2E_TF_lassomyGetDknobjectiveobjective_tau

Dependencies:cobsDEoptimRisotonelatticeMASSMatrixMatrixModelsncvregnnlsosqpquantregR6RcpprobustbasesignalSparseMsurvival

Introduction to the L2E Package (version 2.0)

Xiaoqian Liu, Jocelyn T. Chi, Lisa Lin, Kennth Lange, and Eric C. Chi

Rendered froml2e-intro.Rmdusingknitr::rmarkdownon Sep 17 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Bank databank
Cross validation for L2E sparse regression with distance penalizationCV_L2E_sparse_dist
Cross validation for L2E sparse regression with existing penalization methodsCV_L2E_sparse_ncv
Cross validation for L2E trend filtering regression with distance penalizationCV_L2E_TF_dist
Cross validation for L2E trend filtering regression with Lasso penalizationCV_L2E_TF_lasso
L2EL2E
L2E convex regressionL2E_convex
L2E isotonic regressionL2E_isotonic
L2E multivariate regressionL2E_multivariate
L2E multivariate regression - PGl2e_regression
L2E convex regression - PGl2e_regression_convex
L2E convex regression - MMl2e_regression_convex_MM
L2E isotonic regression - PGl2e_regression_isotonic
L2E isotonic regression - MMl2e_regression_isotonic_MM
L2E multivariate regression - MMl2e_regression_MM
L2E sparse regression with distance penalizationl2e_regression_sparse_dist
L2E sparse regression with existing penalization methodsl2e_regression_sparse_ncv
L2E trend filtering regression with distance penalizationl2e_regression_TF_dist
L2E trend filtering regression with Lasso penalizationl2e_regression_TF_lasso
Solution path of L2E sparse regression with distance penalizationL2E_sparse_dist
Solution path of L2E sparse regression with existing penalization methodsL2E_sparse_ncv
Solution path of the L2E trend filtering regression with distance penalizationL2E_TF_dist
Solution path of the L2E trend filtering regression with LassoL2E_TF_lasso
Compute kth order differencing matrixmyGetDkn
Objective function of the L2E regression - etaobjective
Objective function of the L2E regression - tauobjective_tau
Beta update in L2E convex regression - PGupdate_beta_convex
Beta update in L2E isotonic regression - PGupdate_beta_isotonic
Beta update in L2E convex regression - MMupdate_beta_MM_convex
Beta update in L2E isotonic regression - MMupdate_beta_MM_isotonic
Beta update in L2E multivariate regression - MMupdate_beta_MM_ls
Beta update in L2E sparse regression - MMupdate_beta_MM_sparse
Beta update in L2E trend filtering regression - MMupdate_beta_MM_TF
Beta update in L2E multivariate regression - PGupdate_beta_qr
Beta update in L2E sparse regression - NCVupdate_beta_sparse_ncv
Beta update in L2E trend filtering regression using Lassoupdate_beta_TF_lasso
Eta update using Newton's method with backtrackingupdate_eta_bktk
Tau update functionupdate_tau_R