networktree: Recursive Partitioning of Network Models

Network trees recursively partition the data with respect to covariates. Two network tree algorithms are available: model-based trees based on a multivariate normal model and nonparametric trees based on covariance structures. After partitioning, correlation-based networks (psychometric networks) can be fit on the partitioned data. For details see Jones, Mair, Simon, & Zeileis (2020) <doi:10.1007/s11336-020-09731-4>.

Version: 1.0.1
Depends: R (≥ 3.5.0)
Imports: partykit, qgraph, stats, utils, Matrix, mvtnorm, Formula, grid, graphics, gridBase, reshape2
Suggests: R.rsp, knitr, rmarkdown, fxregime, zoo
Published: 2021-02-04
Author: Payton Jones ORCID iD [aut, cre], Thorsten Simon ORCID iD [aut], Achim Zeileis ORCID iD [aut]
Maintainer: Payton Jones <paytonjjones at gmail.com>
BugReports: https://github.com/paytonjjones/networktree/issues
License: GPL-2 | GPL-3
URL: https://paytonjjones.github.io/networktree/
NeedsCompilation: no
Citation: networktree citation info
Materials: NEWS
In views: Psychometrics
CRAN checks: networktree results

Downloads:

Reference manual: networktree.pdf
Package source: networktree_1.0.1.tar.gz
Windows binaries: r-devel: networktree_1.0.1.zip, r-release: networktree_1.0.1.zip, r-oldrel: networktree_1.0.1.zip
macOS binaries: r-release: networktree_1.0.1.tgz, r-oldrel: networktree_1.0.1.tgz
Old sources: networktree archive

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