Package: bang 1.0.4
bang: Bayesian Analysis, No Gibbs
Provides functions for the Bayesian analysis of some simple commonly-used models, without using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution, using the generalized ratio-of-uniforms method. See Wakefield, Gelfand and Smith (1991) <doi:10.1007/BF01889987> for details. At the moment three conjugate hierarchical models are available: beta-binomial, gamma-Poisson and a 1-way analysis of variance (ANOVA).
Authors:
bang_1.0.4.tar.gz
bang_1.0.4.zip(r-4.5)bang_1.0.4.zip(r-4.4)bang_1.0.4.zip(r-4.3)
bang_1.0.4.tgz(r-4.4-any)bang_1.0.4.tgz(r-4.3-any)
bang_1.0.4.tar.gz(r-4.5-noble)bang_1.0.4.tar.gz(r-4.4-noble)
bang_1.0.4.tgz(r-4.4-emscripten)bang_1.0.4.tgz(r-4.3-emscripten)
bang.pdf |bang.html✨
bang/json (API)
NEWS
| # Install 'bang' in R: |
| install.packages('bang', repos = c('https://paulnorthrop.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/paulnorthrop/bang/issues
- coagulation - Coagulation time data
- pump - Pump-failure data
- rat - Rat tumor data
- temp1 - Mid 21st Century Global Temperature Projection Data
- temp2 - Late 21st Century Global Temperature Projection Data
- weight_gain - Weight Gained by Rats
anovabayesianbetabinomialgammagibbshierarchicalpoisson
Last updated 4 months agofrom:24aaa5f0c2. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 14 2024 |
| R-4.5-win | OK | Nov 14 2024 |
| R-4.5-linux | OK | Nov 14 2024 |
| R-4.4-win | OK | Nov 14 2024 |
| R-4.4-mac | OK | Nov 14 2024 |
| R-4.3-win | OK | Nov 14 2024 |
| R-4.3-mac | OK | Nov 14 2024 |
Exports:hanova1hefpp_checkset_user_priorsim_pred_beta_binomsim_pred_gamma_poissim_pred_hanova1
Dependencies:abindbackportsbayesplotcheckmateclicolorspacedistributionaldplyrfansifarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorR6RColorBrewerRcppRcppArmadilloreshape2rlangrustscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr
Conjugate Hierarchical Models
Rendered frombang-b-hef-vignette.Rmdusingknitr::rmarkdownon Nov 14 2024.Last update: 2020-02-24
Started: 2020-02-21
Hierarchical 1-way Analysis of Variance
Rendered frombang-c-anova-vignette.Rmdusingknitr::rmarkdownon Nov 14 2024.Last update: 2020-02-24
Started: 2020-02-21
Introducing bang: Bayesian Analysis, No Gibbs
Rendered frombang-a-vignette.Rmdusingknitr::rmarkdownon Nov 14 2024.Last update: 2020-02-24
Started: 2020-02-21
Posterior Predictive Checking
Rendered frombang-d-ppc-vignette.Rmdusingknitr::rmarkdownon Nov 14 2024.Last update: 2020-02-24
Started: 2020-02-21
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| bang: Bayesian Analysis, No Gibbs | bang-package bang |
| Coagulation time data | coagulation |
| Posterior sampling for a 1-way hierarchical ANOVA | hanova1 |
| Hierarchical Exponential Family Model | hef |
| Plot diagnostics for a hef object | plot.hef |
| Posterior predictive checks for a hef object | pp_check pp_check.hef |
| Print method for objects of class "hef" | print.hef |
| Print method for objects of class "summary.hef" | print.summary.hef |
| Pump-failure data | pump |
| Rat tumor data | rat |
| Set a user-defined prior | set_user_prior |
| Simulate from a beta-binomial posterior predictive distribution | sim_pred_beta_binom |
| Simulate from a gamma-Poisson posterior predictive distribution | sim_pred_gamma_pois |
| Simulate from a one-way hierarchical ANOVA posterior predictive distribution | sim_pred_hanova1 |
| Summarizing hef objects | summary.hef |
| Mid 21st Century Global Temperature Projection Data | temp1 |
| Late 21st Century Global Temperature Projection Data | temp2 |
| Weight Gained by Rats | weight_gain |
