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 |
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 |
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 (arm64): networktree_1.0.1.tgz, r-release (x86_64): networktree_1.0.1.tgz, r-oldrel: networktree_1.0.1.tgz |
Old sources: | networktree archive |
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