R Observatory: Metrics for grmtree version 0.3.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

grmtree

0.3.0Package record

Recursive Partitioning for Graded Response Models

0packages depend

2.3Kdownloads / year

59.9%test coverage

13/13flavors OK

Overview

About

Maintained by Olayinka I. ArimoroCRAN page ↗GitHub ↗Issues ↗

Provides methods for recursive partitioning based on the 'Graded Response Model' ('GRM'), extending the 'MOB' algorithm from the 'partykit' package. The package allows for fitting 'GRM' trees that partition the population into homogeneous subgroups based on item response patterns and covariates. Includes specialized plotting functions for visualizing 'GRM' trees with different terminal node displays (threshold regions, parameter profiles, and factor score distributions). The package also implements the Longitudinal GRMTree for detecting response shift in PROMs measured at two time points, embedding a constrained two-factor longitudinal GRM within recursive partitioning, with post-hoc characterization of recalibration and reprioritization. Random-forest ensembles (`grmforest()`) with permutation variable importance are available for both the cross-sectional and longitudinal trees. For more details on the methods, see Samejima (1969) doi:10.1002/J.2333-8504.1968.TB00153.X, Komboz et al. (2018) doi:10.1177/0013164416664394 and Arimoro et al. (2025) doi:10.1007/s11136-025-04018-6.

Changes in 0.3.0

New features

grmforest() gains tree_fun and tree_args arguments so one forest engine serves both trees. The default (tree_fun = grmtree) grows a cross-sectional GRM forest exactly as before; tree_fun = longitudinal_grmtree with tree_args = list(n_items = ...) grows a longitudinal GRM forest for response-shift detection. varimp() detects the tree type and scores out-of-bag respondents with the appropriate marginal likelihood, the two-occasion correlated-factor likelihood for longitudinal forests, so the same varimp() call ranks covariates for either forest.
longitudinal_grmtree() now freely estimates the follow-up latent variance (previously fixed to 1), so the two-factor model captures change in trait dispersion across occasions as well as in the mean. The variance remains a structural parameter held out of the split test.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("grmtree", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("Predicare1/grmtree")

Development version from Predicare1/grmtree. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-grmtree

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-grmtree

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-grmtree

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 4.1 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-07-14
ERROR9 OK, 4 ERROR2026-07-11
OK13 OK2026-05-02

Show 2 earlier changesHide 2 earlier changes

ERROR11 OK, 1 ERROR2026-04-25
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.3.0

READMEYes · 745 wordsVignettesYes · 3 · dynamicNEWSYes · 100% structured · mentions 0.3.0Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 34 (4 internal)
Examples that run: 74%20 run in a plain check (16 of them under a condition) · 7 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 12%

Vignettes (3)

How these are measured

Downloads

2.3K

CRAN downloads in the past year

Rank #11,093 · ~6/day · ~189/mo

Releases R versions

Daily download trend is not available in this view yet.

30530 days

94590 days

2.3K1 year

Compare downloads with other packages →

Also on76 r2u1 autocran

How downloads are counted

Repository

predicare1/grmtree ↗Account: Predicare1

1Stars

0Forks

0Open issues

0Open PRs

0Releases

35Commits

2Commit identities

35 commits · Last activity 2026-09-01 · 0% stars, 30d

Responsiveness

not measured

Stars over time

2026-07-07 · 02026-09-06 · 1

How these are measured

PracticesBeta

7 development-tooling and community-health practices detected across 4 families in the upstream repository

Checks run against github.com/predicare1/grmtree on 2026-10-18.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Reproducibility and dev environment (1)

data-raw/

CRAN release process (3)

cran-comments.mdrevdep/CRAN-SUBMISSION

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

15 external dependencies (excludes base and recommended)

Installing grmtree adds 81 packages (39 with compiled code, 45 MB of source tarballs). Heaviest: ggplot2 6.0 MB, SimDesign 2.4 MB, partykit 2.1 MB.

Depends (3)

R >= 4.1.0partykit mirt

Imports (8)

statsgraphicsgridggplot2 rlang strucchangeparallelmvtnorm

LinkingTo (0)

none

Suggests (9)

hlt dplyr magrittr testthat knitr rmarkdown psychotools psychotree psych

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (6)

Olayinka I. Arimoro

Author, Maintainer since 0.1.0 (2026)

Author

Author

Contributor

Contributor

Contributor

Releases (4)

Pick two releases to compare their code metrics.

0.3.0Latest
record
2026-09-01 · current release · diff ↗
0.2.2
record
2026-08-23 · diff ↗
R
R 4.6.0 released · 2026-04-24
0.2.0
record
2026-01-14 · diff ↗ ·
added 10 exports
discrpar_longitudinal_grmtree, fscores_longitudinal_grmtree, itempar_longitudinal_grmtree, latentpar_longitudinal_grmtree, longitudinal_grmtree, plot_rs_heatmap, plot_rs_tree, prepare_longitudinal_data, rs_characterize, threshpar_longitudinal_grmtree
0.1.0
record
2026-01-14
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2026-01-14
Total releases: 4 / 1 yrs
License: GPL-3 OSI
Minimum R: ≥ 4.1.0
Bundled data: 43 KB / 2 files
Download size: 334 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("grmtree")

Format

Arimoro, O. I., James, M. T., Lix, L. M., Sajobi, T. T., Santana, M., & Ugochukwu, E. (2026). grmtree: Recursive Partitioning for Graded Response Models (Version 0.3.0) [Computer software]. https://doi.org/10.32614/CRAN.package.grmtree

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for grmtree version 0.3.0 [Data set]. HJJB, LLC. Data release v2026-10-20. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-20, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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