Flexible Modeling of Count Data
Overview
For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) doi:10.1214/20-EJS1707, Kowal and Wu (2022) doi:10.1111/biom.13617, King and Kowal (2023) doi:10.1214/23-BA1394, and Kowal and Wu (2023) doi:10.48550/arXiv.2110.12316.
- Added defensive checks and conditional evaluation across the package vignette and examples to gracefully handle failures in
Suggestsdependencies. - Fixed package check errors on specific CRAN test environments.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-26
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-04-22
- ERROR13 OK, 1 ERROR2026-04-18
- OK14 OK2026-04-08
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- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 71 (37 internal)
- Examples that run
- 83%20 run in a plain check (2 of them under a condition) · 4 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 71%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 6%
Downloads
Repository
- PRs merged (all time)
- not measured
- Median days to close, 50 most recently updated closed issues
- 3
- Median days to close, 50 most recently updated closed PRs
- not measured
Stars over time
Forks over time
Releases over time
Issues over time
Commits over time
8 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/bking124/countstar on 2026-10-21.
GitHub Pages site last deployed 2026-08-20 · The repository has a site deploy workflow
pkgdown site last built 2026-08-20, before release 1.2.1 (2026-08-21)
Dependencies
Installing countSTAR adds 2 packages (2 with compiled code, 2.8 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.2.1Latest
- 1.2.0
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- unarchivedReturned to CRAN2023-06-30
- 1.0.22023-06-30 · diff ↗
- archivedRemoved from CRAN2023-05-31issues were not corrected in time
- RR 4.3.0 released · 2023-04-21
- 1.0.12023-04-10
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2023-04-10
- Total releases
- 4 / 3 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 1.5 KB / 1 file
- Download size
- 169 KB
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