Package: modelbpp 0.3.0.5

modelbpp: Model BIC Posterior Probability

Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities (BPP), using the method presented in Wu, Cheung, and Leung (2020) <doi:10.1080/00273171.2019.1574546>. See Pesigan, Cheung, Wu, Chang, and Leung (2026) <doi:10.3758/s13428-025-02921-x> for an introduction to the package.

Authors:Shu Fai Cheung [aut, cre], Huiping Wu [aut], Shing On Leung [aut], Ivan Jacob Agaloos Pesigan [ctb]

modelbpp_0.3.0.5.tar.gz
modelbpp_0.3.0.5.zip(r-4.7)modelbpp_0.3.0.5.zip(r-4.6)modelbpp_0.3.0.5.zip(r-4.5)
modelbpp_0.3.0.5.tgz(r-4.6-any)modelbpp_0.3.0.5.tgz(r-4.5-any)
modelbpp_0.3.0.5.tar.gz(r-4.7-any)modelbpp_0.3.0.5.tar.gz(r-4.6-any)
modelbpp_0.3.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
modelbpp/json (API)

# Install 'modelbpp' in R:

install.packages('modelbpp', repos = c('https://sfcheung.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sfcheung/modelbpp/issues

Pkgdown/docs site:https://sfcheung.github.io

Datasets:

dat_cfa - A Sample Dataset Based On a Confirmatory Factor Analysis Model
dat_path_model - A Sample Dataset Based on a Path Model
dat_path_model_p06 - A Sample Dataset Based On a Complex Path Model
dat_sem - A Sample Dataset Based On a Structural Model
dat_serial_4 - A Sample Dataset Based On a Serial Mediation Model
dat_serial_4_weak - A Sample Dataset Based On a Serial Mediation Model With Weak Paths

On CRAN:

lavaan model-comparison model-comparison-and-selection model-selection structural-equation-modeling

4.65 score 3 scripts 460 downloads 15 exports 36 dependencies

Last updated from:9a1eccb7ce. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	189
source / vignettes	OK	172
linux-release-x86_64	OK	185
macos-release-arm64	OK	125
macos-oldrel-arm64	OK	156
windows-devel	OK	156
windows-release	OK	152
windows-oldrel	OK	167
wasm-release	OK	117

Exports:fit_many gen_models get_add get_drop get_partables identical_partables is_partable measurement_invariance_models min_prior model_graph model_set model_set_combined partables_drop same_variables to_partables

Dependencies:boot cli cpp11 farver ggplot2 glue GPArotation gtable igraph isoband labeling lattice lavaan lifecycle lmhelprs magrittr manymome MASS Matrix mnormt nlme numDeriv pbapply pbivnorm pkgconfig psych quadprog R6 RColorBrewer rlang S7 scales semTools vctrs viridisLite withr

Get Started

Rendered frommodelbpp.Rmdusingknitr::rmarkdown

Last update: 2026-05-13
Started: 2023-06-18

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Manipulate Parameter Tables	c.model_set c.partables manipulate_partables partables_drop
A Sample Dataset Based On a Confirmatory Factor Analysis Model (For Testing)	dat_cfa
A Sample Dataset Based on a Path Model (For Testing)	dat_path_model
A Sample Dataset Based On a Complex Path Model (For Testing)	dat_path_model_p06
A Sample Dataset Based On a Structural Model (For Testing)	dat_sem
A Sample Dataset Based On a Serial Mediation Model (For Testing)	dat_serial_4
A Sample Dataset Based On a Serial Mediation Model With Weak Paths (For Testing)	dat_serial_4_weak
Fit a List of Models	fit_many
Models That Are Less Restricted	get_add
Models That Are More Restricted	get_drop
Measurement Invariance Models	measurement_invariance_models
Minimum Prior	min_prior
Generate a Graph of Models	model_graph
BIC Posterior Probabilities of Neighboring Models	gen_models model_set
Two or More Hypothesized Models	model_set_combined
Helper Functions For 'partables'-Class Objects	get_partables identical_partables is_partable partables_helpers same_variables to_partables
Plot a Network of Models	plot.model_graph
Print a 'model_set'-Class Object	print.model_set
Print a 'partables'-Class Object	print.partables
Print an 'sem_outs'-Class Object	print.sem_outs