Package: msaenet 3.1.2.9000

msaenet: Multi-Step Adaptive Estimation Methods for Sparse Regressions

Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <doi:10.1080/00949655.2015.1016944>, with support for multi-step adaptive MCP-net (MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.

Authors:Nan Xiao [aut, cre], Qing-Song Xu [aut]

msaenet_3.1.2.9000.tar.gz
msaenet_3.1.2.9000.zip(r-4.7)msaenet_3.1.2.9000.zip(r-4.6)msaenet_3.1.2.9000.zip(r-4.5)
msaenet_3.1.2.9000.tgz(r-4.6-any)msaenet_3.1.2.9000.tgz(r-4.5-any)
msaenet_3.1.2.9000.tar.gz(r-4.7-any)msaenet_3.1.2.9000.tar.gz(r-4.6-any)
msaenet_3.1.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
msaenet/json (API)

# Install 'msaenet' in R:

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

Bug tracker:https://github.com/nanxstats/msaenet/issues

Pkgdown/docs site:https://nanx.me

On CRAN:

false-positive-control high-dimensional-data linear-regression machine-learning variable-selection

7.64 score 13 stars 6 packages 65 scripts 5.7k downloads 1 mentions 19 exports 12 dependencies

Last updated from:8cb5ef6ffc. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	130
source / vignettes	OK	198
linux-release-x86_64	OK	147
macos-release-arm64	OK	95
macos-oldrel-arm64	OK	113
windows-devel	OK	121
windows-release	OK	102
windows-oldrel	OK	106
wasm-release	OK	116

Exports:aenet amnet asnet msaenet msaenet.fn msaenet.fp msaenet.mae msaenet.mse msaenet.nzv msaenet.nzv.all msaenet.rmse msaenet.rmsle msaenet.sim.binomial msaenet.sim.cox msaenet.sim.gaussian msaenet.sim.poisson msaenet.tp msamnet msasnet

Dependencies:codetools foreach glmnet iterators lattice Matrix mvtnorm ncvreg Rcpp RcppEigen shape survival

A Quick Introduction to msaenet

Introduction | Walkthrough | Summary | References

Rendered frommsaenet.Rmdusingknitr::rmarkdown

Last update: 2024-05-10
Started: 2016-09-20

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Adaptive Elastic-Net	aenet
Adaptive MCP-Net	amnet
Adaptive SCAD-Net	asnet
Extract Model Coefficients	coef.msaenet
Multi-Step Adaptive Elastic-Net	msaenet
Get the Number of False Negative Selections	msaenet.fn
Get the Number of False Positive Selections	msaenet.fp
Mean Absolute Error (MAE)	msaenet.mae
Mean Squared Error (MSE)	msaenet.mse
Get Indices of Non-Zero Variables	msaenet.nzv
Get Indices of Non-Zero Variables in All Steps	msaenet.nzv.all
Root Mean Squared Error (RMSE)	msaenet.rmse
Root Mean Squared Logarithmic Error (RMSLE)	msaenet.rmsle
Generate Simulation Data for Benchmarking Sparse Regressions (Binomial Response)	msaenet.sim.binomial
Generate Simulation Data for Benchmarking Sparse Regressions (Cox Model)	msaenet.sim.cox
Generate Simulation Data for Benchmarking Sparse Regressions (Gaussian Response)	msaenet.sim.gaussian
Generate Simulation Data for Benchmarking Sparse Regressions (Poisson Response)	msaenet.sim.poisson
Get the Number of True Positive Selections	msaenet.tp
Multi-Step Adaptive MCP-Net	msamnet
Multi-Step Adaptive SCAD-Net	msasnet
Plot msaenet Model Objects	plot.msaenet
Make Predictions from an msaenet Model	predict.msaenet
Print msaenet Model Information	print.msaenet