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:

if (!require("remotes")) {
  install.packages("remotes")
}
remotes::install_gitlab("nicolas.verzelen/blockmodels4inventories", 
                        host = "forgemia.inra.fr",
                        build_vignettes=TRUE)

Usage

Once the package is installed on your computer, it can be loaded into a R session:

library(blockmodels4inventories)
help(package="blockmodels4inventories")

Main specifications:

  • LBM as a model-based bi-clustering method
  • Easily handles covariates

incid

biclust

count

Citation

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:

citation("blockmodels4inventories")

See also citation() for citing R itself.