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cff-version: 1.2.0
message: 'To cite package "BClustLonG" in publications use:'
type: software
license: GPL-2.0-only
title: 'BClustLonG: A Dirichlet Process Mixture Model for Clustering Longitudinal
  Gene Expression Data'
version: 0.1.3
doi: 10.32614/CRAN.package.BClustLonG
abstract: Many clustering methods have been proposed, but most of them cannot work
  for longitudinal gene expression data. 'BClustLonG' is a package that allows us
  to perform clustering analysis for longitudinal gene expression data. It adopts
  a linear-mixed effects framework to model the trajectory of genes over time, while
  clustering is jointly conducted based on the regression coefficients obtained from
  all genes. To account for the correlations among genes and alleviate the high dimensionality
  challenges, factor analysis models are adopted for the regression coefficients.
  The Dirichlet process prior distribution is utilized for the means of the regression
  coefficients to induce clustering. This package allows users to specify which variables
  to use for clustering (intercepts or slopes or both) and whether a factor analysis
  model is desired. More details about this method can be found in Jiehuan Sun, et
  al. (2017) <https://doi.org/10.1002/sim.7374>.
authors:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com
- family-names: Herazo-Maya
  given-names: Jose D.
- family-names: Kaminski
  given-names: Naftali
- family-names: Zhao
  given-names: Hongyu
- family-names: Warren
  given-names: Joshua L.
repository: https://jjr1234.r-universe.dev
commit: 088916fda0b0f73bd15ebee9a5451b26678d1949
date-released: '2020-05-07'
contact:
- family-names: Sun
  given-names: Jiehuan
  email: jiehuan.sun@gmail.com