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cff-version: 1.2.0
message: 'To cite package "HGMND" in publications use:'
type: software
license: GPL-3.0-only
title: 'HGMND: Heterogeneous Graphical Model for Non-Negative Data'
version: 0.1.0
doi: 10.32614/CRAN.package.HGMND
abstract: Graphical model is an informative and powerful tool to explore the conditional
  dependence relationships among variables. The traditional Gaussian graphical model
  and its extensions either have a Gaussian assumption on the data distribution or
  assume the data are homogeneous. However, there are data with complex distributions
  violating these two assumptions. For example, the air pollutant concentration records
  are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions
  of these concentration records in different months of a year can be far different,
  which means it is uncertain whether datasets from different months are homogeneous.
  Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional
  dependence relationships among variables. Therefore, we propose a heterogeneous
  graphical model for non-negative data (HGMND) to simultaneously cluster multiple
  datasets and estimate the conditional dependence matrix of variables from a non-Gaussian
  and non-negative exponential family in each cluster.
authors:
- family-names: Zhang
  given-names: Jiaqi
  email: boarzhang@gmail.com
- family-names: Fan
  given-names: Xinyan
- family-names: Li
  given-names: Yang
repository: https://zjq-ruc.r-universe.dev
commit: 40027c4d77d1ea60f80abb66bc91717f6ad1c811
date-released: '2021-04-19'
contact:
- family-names: Zhang
  given-names: Jiaqi
  email: boarzhang@gmail.com