`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.

You can install the released version of epca from CRAN with:

`install.packages("epca")`

or the development version from GitHub with:

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

The usage of `sca`

and `sma`

is
straightforward. For example, to find `k`

sparse PCs of a
data matrix `X`

:

`sca(X, k)`

Similarly, we can find a rank-`k`

sparse matrix
decomposition by

`sma(X, k)`

For more examples, please see the vignette:

`vignette("epca")`

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

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