# Exploratory Principal Component Analysis

`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

`epca`

is not yet on CRAN. You could install the development version from GitHub with:

```
# 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`

:

Similarly, we can find a rank-`k`

sparse matrix decomposition by

For more examples, please see the vignette:

## Getting help

If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.

## Reference

Chen F and Rohe K, “A New Basis for Sparse PCA.”