README.qmd

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format: gfm
---

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```{r}
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knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# slcm

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The goal of `slcm` is to provide an implementation of the exploratory 
Sparse Latent Class Model (SLCM) for Binary Data described by Chen, Y., 
Culpepper, S. A., and Liang, F. (2020) <doi:10.1007/s11336-019-09693-2>.

This package contains a new implementation of the proposed SLCM based on the
paper. You may find original papers implementation in the
[`inst/` folder](https://github.com/tmsalab/slcm/tree/main/inst) of the package.

## Installation

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

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

Or, you can be on the cutting-edge development version on
[GitHub](https://github.com/) using:

``` r
# install.packages("devtools")
devtools::install_github("tmsalab/slcm")
```

## Usage

To use `slcm`, load the package using:

```r
library("slcm")
```

From here, the SLCM model can be estimated using:

```r
model_slcm = slcm::slcm(
  y = <data>,
  k = <k>
)
```

## Authors

James Joseph Balamuta and Steven Andrew Culpepper 

## Citing the `slcm` package

To ensure future development of the package, please cite `slcm`
package if used during an analysis or simulation study. Citation
information for the package may be acquired by using in *R*:

``` r
citation("slcm")
```

## License

GPL (\>= 2)