README.Rmd

---
# date: "`r Sys.Date()`"
output: github_document
header-includes:
  - \usepackage{amsfonts,amsmath,amsthm,amsbsy,amssymb,dsfont,bm,mathtools,mathalfa}
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<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  tidy = TRUE, 
  tidy.opts = list(comment = FALSE),
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
library(epca)
```

# Exploratory Principal Component Analysis

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`epca` is an R package for comprehending any data matrix that contains *low-rank* and *sparse* underlying signals of interest. 
The package currently features two key tools:

* `sca` for **s**parse principal **c**omponent **a**nalysis.
* `sma` for **s**parse **m**atrix **a**pproximation, a two-way data analysis for simultaneously row and column dimensionality reductions.

## Installation

You can install the released version of epca from [CRAN](https://CRAN.R-project.org) with:

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

or the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("fchen365/epca")
```
## Example

The usage of `sca` and `sma` is straightforward. For example, to find `k` sparse PCs of a data matrix `X`:
```{r sca, eval=FALSE}
## perform sparse PCA
sca(X, k)
```

Similarly, we can find a rank-`k` sparse matrix decomposition by
```{r sma, eval=FALSE}
## perform sparse matrix approximation
sma(X, k)
```

For more examples, please see the vignette: 
```{r, eval=FALSE}
vignette("epca")
```


## Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on [GitHub](https://github.com/fchen365/epca/issues). 


## Reference

Chen F and Rohe K, "A New Basis for Sparse Principal Component Analysis." ([arXiv](https://arxiv.org/abs/2007.00596))