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HGMND
View on CRAN: Click
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Download and install HGMND package within the R console
Install from CRAN:
install.packages("HGMND")
Install from Github:
library("remotes")
install_github("cran/HGMND")
Install by package version:
library("remotes")
install_version("HGMND", "0.1.0")
Attach the package and use:
library("HGMND")
Maintained by
Jiaqi Zhang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-04-19
Latest Update: 2021-04-19
Description:
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.
How to cite:
Jiaqi Zhang (2021). HGMND: Heterogeneous Graphical Model for Non-Negative Data. R package version 0.1.0, https://cran.r-project.org/web/packages/HGMND. Accessed 21 Nov. 2024.
Previous versions and publish date:
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Complete documentation for HGMND
Functions, R codes and Examples using
the HGMND R package
Some associated functions: HGMND . HGMND_SimuData . getCluster .
Some associated R codes: HGMND.R . getCluster.R . Full HGMND package functions and examples
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