README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{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 <img src="man/figures/logo.png" align="right" width="120" />

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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.