Package: LearnBayes 2.17
LearnBayes: Learning Bayesian Inference
Contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Authors:
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LearnBayes.pdf |LearnBayes.html✨
LearnBayes/json (API)
# Install 'LearnBayes' in R: |
install.packages('LearnBayes', repos = c('https://bayesball.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bayesball/learnbayes/issues
- achievement - School achievement data
- baseball.1964 - Team records in the 1964 National League baseball season
- bermuda.grass - Bermuda grass experiment data
- birdextinct - Bird measurements from British islands
- birthweight - Birthweight regression study
- breastcancer - Survival experience of women with breast cancer under treatment
- calculus.grades - Calculus grades dataset
- cancermortality - Cancer mortality data
- chemotherapy - Chemotherapy treatment effects on ovarian cancer
- darwin - Darwin's data on plants
- donner - Donner survival study
- election - Florida election data
- election.2008 - Poll data from 2008 U.S. Presidential Election
- footballscores - Game outcomes and point spreads for American football
- hearttransplants - Heart transplant mortality data
- iowagpa - Admissions data for an university
- jeter2004 - Hitting data for Derek Jeter
- marathontimes - Marathon running times
- puffin - Bird measurements from British islands
- schmidt - Batting data for Mike Schmidt
- sluggerdata - Hitting statistics for ten great baseball players
- soccergoals - Goals scored by professional soccer team
- stanfordheart - Data from Stanford Heart Transplanation Program
- strikeout - Baseball strikeout data
- studentdata - Student dataset
Last updated 7 years agofrom:f7722076d0. Checks:ERROR: 1 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | FAIL | Nov 11 2024 |
R-4.5-win | WARNING | Nov 11 2024 |
R-4.5-linux | WARNING | Nov 11 2024 |
R-4.4-win | WARNING | Nov 11 2024 |
R-4.4-mac | WARNING | Nov 11 2024 |
R-4.3-win | WARNING | Nov 11 2024 |
R-4.3-mac | WARNING | Nov 11 2024 |
Exports:bayes.influencebayes.model.selectionbayes.probitbayesresidualsbeta.selectbetabinexchbetabinexch0bfexchbfindepbinomial.beta.mixblinregblinregexpectedblinregpredbprobit.probsbradley.terry.postcareertraj.setupcauchyerrorpostctablediscintdiscrete.bayesdiscrete.bayes.2dmnormdmtgibbsgroupeddataposthiergibbshistpriorhowardpriorimpsamplingindepmetroplaplacelbinormlogctablepostlogisticpostlogpoissgammalogpoissnormalmnormt.onesidedmnormt.twosidedmycontournormal.normal.mixnormal.selectnormchi2postnormnormexchnormpostprednormpostsimordergibbspbetappbetatpdiscpdiscpplot.bayesplot.bayes2plot.posteriorpoissgamexchpoisson.gamma.mixpredplotprint.bayesprior.two.parametersrdirichletreg.gprior.postregrouprejectsamplingrigammarmnormrmtrobusttrtruncatedrwmetropsimcontoursimulate.posteriorsirsummary.bayessummary.posteriortransplantposttriplotweibullregpost
Introduction to Bayes Factors
Rendered fromBayesFactors.Rnw
usingutils::Sweave
on Nov 11 2024.Last update: 2015-02-16
Started: 2015-02-16
Introduction to Bayes using Discrete Priors
Rendered fromDiscreteBayes.Rnw
usingutils::Sweave
on Nov 11 2024.Last update: 2015-02-16
Started: 2015-02-16
Introduction to Markov Chain Monte Carlo
Rendered fromMCMCintro.Rnw
usingutils::Sweave
on Nov 11 2024.Last update: 2015-02-16
Started: 2015-02-16
Introduction to Multilevel Modeling
Rendered fromMultilevelModeling.Rnw
usingutils::Sweave
on Nov 11 2024.Last update: 2015-02-16
Started: 2015-02-16
Learning About a Binomial Proportion
Rendered fromBinomialInference.Rnw
usingutils::Sweave
on Nov 11 2024.Last update: 2015-02-16
Started: 2015-02-16