msaenet

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

https://github.com/nanxstats/msaenet

Science Score: 39.0%

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Found 1 DOI reference(s) in README
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Low similarity (18.1%) to scientific vocabulary

Keywords

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

Last synced: 6 months ago · JSON representation

Repository

🧲 Multi-step adaptive estimation for reducing false positive selection in sparse regressions

Basic Info

Host: GitHub
Owner: nanxstats
License: gpl-3.0
Language: R
Default Branch: master
Homepage: https://nanx.me/msaenet/
Size: 13.5 MB

Statistics

Stars: 13
Watchers: 4
Forks: 7
Open Issues: 6
Releases: 16

Topics

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

Created over 9 years ago · Last pushed over 1 year ago

Metadata Files

Readme Changelog Contributing License Code of conduct

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::knit_hooks$set(pngquant = knitr::hook_pngquant)

knitr::opts_chunk$set(
  echo = FALSE,
  message = FALSE,
  warning = FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  dev = "ragg_png",
  dpi = 150,
  fig.retina = 2,
  fig.width = 10,
  fig.height = 5,
  out.width = "100%",
  pngquant = "--speed=1 --quality=80"
)
```

# msaenet 


[![R-CMD-check](https://github.com/nanxstats/msaenet/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/nanxstats/msaenet/actions/workflows/R-CMD-check.yaml)
[![CRAN status](https://www.r-pkg.org/badges/version/msaenet)](https://cran.r-project.org/package=msaenet)
[![CRAN downloads](https://cranlogs.r-pkg.org/badges/msaenet)](https://cran.r-project.org/package=msaenet)


msaenet implements the multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) [[PDF](https://nanx.me/papers/msaenet.pdf)].

Nonconvex multi-step adaptive estimations based on MCP-net or SCAD-net are also supported.

Check `vignette("msaenet")` to get started.

## Installation

You can install msaenet from CRAN:

```r
install.packages("msaenet")
```

Or try the development version on GitHub:

```r
remotes::install_github("nanxstats/msaenet")
```

## Citation

To cite the msaenet package in publications, please use

> Nan Xiao and Qing-Song Xu. (2015). Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection. _Journal of Statistical Computation and Simulation_ 85(18), 3755--3765.

A BibTeX entry for LaTeX users is

```bibtex
@article{xiao2015multi,
  title   = {Multi-step adaptive elastic-net: reducing false positives in high-dimensional variable selection},
  author  = {Nan Xiao and Qing-Song Xu},
  journal = {Journal of Statistical Computation and Simulation},
  volume  = {85},
  number  = {18},
  pages   = {3755--3765},
  year    = {2015},
  doi     = {10.1080/00949655.2015.1016944}
}
```

## Gallery

### Adaptive Elastic-Net / Multi-Step Adaptive Elastic-Net

```{r}
library("msaenet")

dat <- msaenet.sim.gaussian(
  n = 500, p = 500, rho = 0.8,
  coef = c(rep(-1, 2), rep(1, 3)), snr = 1.5, p.train = 0.5,
  seed = 1001
)
```

```{r}
#| msaenet
aenet.fit <- aenet(
  dat$x.tr, dat$y.tr,
  alphas = seq(0.1, 0.9, 0.1), seed = 1003
)

msaenet.fit <- msaenet(
  dat$x.tr, dat$y.tr,
  alphas = seq(0.1, 0.9, 0.1),
  nsteps = 5L, tune.nsteps = "ebic",
  seed = 1003
)

par(mfrow = c(1, 2))
plot(aenet.fit)
plot(msaenet.fit)
```

### Adaptive MCP-Net / Multi-Step Adaptive MCP-Net

```{r}
#| msamnet
amnet.fit <- amnet(
  dat$x.tr, dat$y.tr,
  alphas = seq(0.1, 0.9, 0.1), seed = 1003
)

msamnet.fit <- msamnet(
  dat$x.tr, dat$y.tr,
  gammas = 3, alphas = seq(0.1, 0.9, 0.1),
  nsteps = 4L, tune.nsteps = "ebic",
  seed = 1003
)

par(mfrow = c(1, 2))
plot(amnet.fit)
plot(msamnet.fit)
```

### Adaptive SCAD-Net / Multi-Step Adaptive SCAD-Net

```{r}
#| msasnet
asnet.fit <- asnet(
  dat$x.tr, dat$y.tr,
  alphas = seq(0.1, 0.9, 0.1), seed = 1003
)

msasnet.fit <- msasnet(
  dat$x.tr, dat$y.tr,
  gammas = 3.7, alphas = seq(0.1, 0.9, 0.1),
  nsteps = 4L, tune.nsteps = "ebic",
  seed = 1003
)

par(mfrow = c(1, 2))
plot(asnet.fit)
plot(msasnet.fit)
```

## Contribute

To contribute to this project, please take a look at the
[Contributing Guidelines](https://nanx.me/msaenet/CONTRIBUTING.html) first.
Please note that the msaenet project is released with a
[Contributor Code of Conduct](https://nanx.me/msaenet/CODE_OF_CONDUCT.html).
By contributing to this project, you agree to abide by its terms.

## License

msaenet is free and open source software, licensed under GPL-3.

Owner

Name: Nan Xiao
Login: nanxstats
Kind: user
Location: Upper Gwynedd, PA
Company: Merck & Co.

Website: https://nanx.me
Twitter: nanxstats
Repositories: 144
Profile: https://github.com/nanxstats

Statistician

GitHub Events

Total

Last Year

Committers

Last synced: over 2 years ago

All Time

Total Commits: 135
Total Committers: 2
Avg Commits per committer: 67.5
Development Distribution Score (DDS): 0.126

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Nan Xiao	r**t@g**m	118
Nan Xiao	me@n****e	17

Committer Domains (Top 20 + Academic)

nanx.me: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 18
Total pull requests: 4
Average time to close issues: 6 months
Average time to close pull requests: 1 minute
Total issue authors: 5
Total pull request authors: 1
Average comments per issue: 1.17
Average comments per pull request: 0.0
Merged pull requests: 4
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 4
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

nanxstats (11)
runnytone (2)
mwh3780 (2)
xingxingyanjing (1)
kevinegan31 (1)

Pull Request Authors

nanxstats (7)

Top Labels

Issue Labels

enhancement (3) chore (1) documentation (1) question (1) bug (1) cran (1)

Pull Request Labels

chore (1)

Packages

Total packages: 1
Total downloads:
- cran 554 last-month
Total docker downloads: 192

Total dependent packages: 1
Total dependent repositories: 1
Total versions: 16
Total maintainers: 1

cran.r-project.org: msaenet

Multi-Step Adaptive Estimation Methods for Sparse Regressions

Homepage: https://nanx.me/msaenet/
Documentation: http://cran.r-project.org/web/packages/msaenet/msaenet.pdf
License: GPL (≥ 3)
Latest release: 3.1.2
published almost 2 years ago

Versions: 16
Dependent Packages: 1
Dependent Repositories: 1
Downloads: 554 Last month
Docker Downloads: 192

Rankings

Forks count: 9.6%

Stargazers count: 14.6%

Average: 23.3%

Dependent repos count: 23.9%

Docker downloads count: 27.4%

Dependent packages count: 28.7%

Downloads: 35.4%

Maintainers (1)

me@nanx.me

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 3.0.2 depends
Matrix * imports
foreach * imports
glmnet * imports
mvtnorm * imports
ncvreg >= 3.8 imports
survival * imports
doParallel * suggests
knitr * suggests
rmarkdown * suggests

.github/workflows/R-CMD-check.yaml actions

actions/checkout v4 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action v4.5.0 composite
actions/checkout v4 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

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msaenet

Science Score: 39.0%

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