Setup: simulate and fit | The diagnostics report | Reading the goodness-of-fit checks | Standalone ESS and split-Rhat | Standalone posterior predictive checks | Reading the report table | Plotting methods | Benchmarking against the unconstrained baseline | A non-trivial example: bipartite matching
Package: combreg 0.2.0
combreg: Bayesian Regression for Combinatorial Response Data
Bayesian regression whose response data are integer-valued vectors subject to combinatorial constraints in the form of Ay<=b. Implements the Metropolis-Hastings-within-Gibbs sampler of Zheng et al. (2026) <doi:10.1080/01621459.2026.2714575>, an unconstrained probit baseline for comparison, benchmarking helpers, MCMC and regression diagnostics (effective sample sizes, split-Rhat, structured reports), plotting methods (trace, autocorrelation, violin, effective-sample-size, sampling-efficiency, residual heat map), and utilities for constraint validation (total unimodularity, feasibility) and data simulation.
Authors:
combreg_0.2.0.tar.gz
combreg_0.2.0.zip(r-4.7-x86_64)combreg_0.2.0.zip(r-4.7-arm64)combreg_0.2.0.zip(r-4.6-x86_64)combreg_0.2.0.zip(r-4.6-arm64)combreg_0.2.0.zip(r-4.5-x86_64)
combreg_0.2.0.tgz(r-4.6-x86_64)combreg_0.2.0.tgz(r-4.6-arm64)combreg_0.2.0.tgz(r-4.5-x86_64)combreg_0.2.0.tgz(r-4.5-arm64)
combreg_0.2.0.tar.gz(r-4.7-arm64)combreg_0.2.0.tar.gz(r-4.7-x86_64)combreg_0.2.0.tar.gz(r-4.6-arm64)combreg_0.2.0.tar.gz(r-4.6-x86_64)
combreg_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
combreg/json (API)
| # Install 'combreg' in R: |
| install.packages('combreg', repos = c('https://yuzh98.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/yuzh98/combreg/issues
Uses libs:
Last updated from:1a400d3145. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 247 | ||
| linux-devel-x86_64 | OK | 253 | ||
| source / vignettes | OK | 200 | ||
| linux-release-arm64 | OK | 241 | ||
| linux-release-x86_64 | OK | 191 | ||
| macos-release-arm64 | OK | 279 | ||
| macos-release-x86_64 | OK | 326 | ||
| macos-oldrel-arm64 | OK | 271 | ||
| macos-oldrel-x86_64 | OK | 616 | ||
| windows-devel-arm64 | OK | 442 | ||
| windows-devel-x86_64 | OK | 316 | ||
| windows-release-arm64 | OK | 433 | ||
| windows-release-x86_64 | OK | 285 | ||
| windows-oldrel-x86_64 | OK | 329 | ||
| wasm-release | OK | 107 |
Exports:coef_precomputecrrcrr_benchmarkcrr_constraintscrr_controlcrr_diagnosticscrr_esscrr_ppccrr_priorcrr_rhatdraw_utilitydual_feasibleinit_dualis_feasibleis_tumrandom_constraintssample_dualsample_utilitysimulate_crrupdate_coef
Dependencies:codalatticeRcppRcppArmadillotruncnorm
Diagnostics and benchmarking
Last update: 2026-07-20
Started: 2026-07-12
Introduction to combreg
The model | Defining constraints | Simulating data and fitting | Why constraints matter | The adaptive zeta_block controller | Customizing the sampler | Formula interface | Building custom models from primitives
Last update: 2026-07-20
Started: 2026-07-12
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Convert to posterior draws_array | as_draws.crr_fit |
| Convert to coda mcmc.list | as.mcmc.crr_fit |
| Posterior mean coefficients | coef.crr_fit |
| Bayesian combinatorial response regression | crr |
| Benchmark samplers on a common data set | crr_benchmark |
| Constraint system for combinatorial responses | crr_constraints |
| Sampler control parameters | crr_control |
| MCMC and regression diagnostics report | crr_diagnostics |
| Effective sample sizes | crr_ess |
| Posterior predictive goodness-of-fit checks | crr_ppc |
| Prior specification for crr models | crr_prior |
| Split-Rhat convergence diagnostics | crr_rhat |
| Fitted responses | fitted.crr_fit |
| Check feasibility of responses | is_feasible |
| Check total unimodularity | is_tum |
| Diagnostic plots for a crr fit | plot.crr_fit |
| Posterior predictions | predict.crr_fit |
| Generate a random totally unimodular constraint system | random_constraints |
| Response residuals | residuals.crr_fit |
| Simulate combinatorial response regression data | simulate_crr |
| Posterior summary of a crr fit | summary.crr_fit |