Package: bmggum 0.1.0.9000

Naidan Tu

bmggum: Bayesian Multidimensional Generalized Graded Unfolding Model

Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>). Functions are provided for estimation, result extraction, model fit statistics, and plottings.

Authors:Naidan Tu [aut, cre], Bo Zhang [aut]

bmggum_0.1.0.9000.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
bmggum/json (API)

# Install 'bmggum' in R:

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

Bug tracker:https://github.com/naidantu/bmggum/issues

Uses libs:

c++– GNU Standard C++ Library v3
openjdk– OpenJDK Java runtime, using Hotspot JIT

On CRAN:

cpp openjdk

4.40 score 5 stars 6 scripts 369 downloads 5 exports 61 dependencies

Last updated from:b53ea74fd7. Checks:11 NOTE, 1 OK, 1 FAIL. Indexed: yes.

Target	Result	Time
linux-devel-arm64	NOTE	516
linux-devel-x86_64	NOTE	553
source / vignettes	OK	838
linux-release-arm64	NOTE	506
linux-release-x86_64	NOTE	564
macos-release-arm64	NOTE	447
macos-release-x86_64	NOTE	922
macos-oldrel-arm64	NOTE	557
macos-oldrel-x86_64	NOTE	805
windows-devel	NOTE	802
windows-release	NOTE	691
windows-oldrel	NOTE	733
wasm-release	FAIL	171

Exports:bayesplot bmggum extract itemplot modfit

Dependencies:abind backports BH callr checkmate cli cpp11 desc distributional edstan farver generics ggplot2 GGUM glue GPArotation gridExtra gtable inline isoband labeling lattice lifecycle loo magrittr matrixStats mnormt nlme numDeriv otel pillar pkgbuild pkgconfig posterior processx ps psych QuickJSR R6 rbibutils RColorBrewer Rcpp RcppEigen RcppParallel Rdpack rJava rlang rstan rstantools S7 scales StanHeaders tensorA tibble utf8 vctrs viridis viridisLite withr xlsx xlsxjars

bmggum: Bayesian Estimation of Multidimensional Generalized Graded Unfolding Model

Overview | Tutorial | Step 1: Input data | Step 2: Estimate using the function bmggum() | Step 3: Extract the estimated results using the function extract() | Step 4: Obtain model fit statistics using the function modfit() | Step 5: Plotting using the function bayesplot() | Step 6: Plotting observable response categories (ORCs) for items using the function itemplot()

Rendered frommy-vignette.Rmdusingknitr::rmarkdown

Last update: 2021-04-02
Started: 2021-01-05

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
The 'bmggum' package.	bmggum-package
bayesian convergence diagnosis plotting function	bayesplot
Bayesian Multidimensional Generalized Graded Unfolding Model (bmggum)	bmggum
results extraction	extract
item plotting function including observable response categories (ORCs)	itemplot
Model fit	modfit