README.Rmd

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output: github_document
---

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```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
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  fig.path = "man/figures/README-",
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)
```

# cobiclust

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The goal of cobiclust is to perform a biclustering of over-dispersed count data such as those produced by amplicon-based sequencing for example.

## Installation

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

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

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

``` r
# install.packages("devtools")
devtools::install_github("julieaubert/cobiclust")
```
## Example

This is a basic example which shows you how to perform a biclustering on simulated data:

```{r example}
library(cobiclust)
## basic example code
# npc <- c(50, 40) # nodes per class
# KG <- c(2, 3) # classes
# nm <- npc * KG # nodes
# Z <- diag( KG[1]) \%x\% matrix(1, npc[1], 1)
# W <- diag(KG[2]) \%x\% matrix(1, npc[2], 1)
# L <- 70 * matrix( runif( KG[1] * KG[2]), KG[1], KG[2])
# M_in_expectation <- Z \%*\% L \%*\% t(W)
# size <- 50
# M<-matrix(
#  rnbinom(
#    n = length(as.vector(M_in_expectation)),
#    mu = as.vector(M_in_expectation), size = size)
#  , nm[1], nm[2])
# rownames(M) <- paste("OTU", 1:nrow(M), sep = "_")
# colnames(M) <- paste("S", 1:ncol(M), sep = "_")
# res <- cobiclust(M, K = 2, G = 3, nu_j = rep(1,120), a = 1/size, cvg_lim = 1e-5)
```