clere

Simultaneous Variables Clustering and Regression

https://github.com/mcanouil/clere

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Simultaneous Variables Clustering and Regression

README.Rmd

---
output: github_document
---



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

# clere: Simultaneous Variables Clustering and Regression


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Implements an empirical Bayes approach for simultaneous variable clustering and regression. 
This version also (re)implements in C++ an R script proposed by Howard Bondell that fits the Pairwise Absolute Clustering and Sparsity (PACS) methodology (see Sharma et al (2013) doi: [10.1080/15533174.2012.707849](https://doi.org/10.1080/15533174.2012.707849)).


## Installation

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

```{r, eval = FALSE}
install.packages("clere")
```

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

```{r, eval = FALSE}
# install.packages("remotes")
remotes::install_github("mcanouil/clere")
```


## Citing clere

```{r, echo = FALSE, results = "asis"}
print(citation("clere"), "html")
```

```{r, echo = FALSE, comment = ""}
print(citation("clere"), "bibtex")
```


## Example

```{r example}
library(clere)

x <- matrix(rnorm(50 * 100), nrow = 50, ncol = 100)
y <- rnorm(50)

model <- fitClere(y = y, x = x, g = 2, plotit = FALSE)
model

plot(model)

clus <- clusters(model, threshold = NULL)
clus

predict(model, newx = x + 1)

summary(model)
```


## Getting help

If you encounter a clear bug, please file a minimal reproducible example on [github](https://github.com/mcanouil/clere/issues).  
For questions and other discussion, please contact the package maintainer.

---

Please note that this project is released with a [Contributor Code of Conduct](https://github.com/mcanouil/clere/blob/main/.github/CODE_OF_CONDUCT.md).  
By participating in this project you agree to abide by its terms.

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