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# blockmodels4inventories
a Collection of R Functions to Analyze Inventories Data
http://nicolas.verzelen.pages.mia.inra.fr/blockmodels4inventories/
Using a latent block model by encapsulating the **blockmodels** library in the **blockmodels4inventories** library to perform biclustering on survey data to characterize the crop diversity and diversity of seeds supply modes..
possible input data: Presence/absence and count data
* on crop diversity
* on crop uses
* on sources of seed supply
as well as . . . many covariates
# Installation
To install the **blockmodels4inventories** package, the easiest is to install it directly from GitLab. Open an R session and run the following commands:
```R
if (!require("remotes")) {
install.packages("remotes")
}
remotes::install_gitlab("nicolas.verzelen/blockmodels4inventories",
```
# Usage
Once the package is installed on your computer, it can be loaded into a R session:
```R
library(blockmodels4inventories)
help(package="blockmodels4inventories")
```
Main specifications:
* LBM as a model-based bi-clustering method
* Easily handles covariates
As a lot of time and effort were spent in creating the **blockmodels4inventories** and **blockmodels** methods, please cite it when using it for data analysis:
Jean-Benoist Leger (2016). Blockmodels: A R-package for estimating in Latent Block Model and Stochastic Block Model, with various probability functions, with or without covariates. arXiv:1602.07587
Jean-Benoist Leger (2015). Blockmodels : Latent and Stochastic Block Model
Estimation by a 'V-EM' Algorithm.
You should also cite the **blockmodels4inventories** package:
```R
citation("blockmodels4inventories")
```
See also citation() for citing R itself.