Package: tidyabc 0.0.1

Robert Challen

tidyabc: Approximate Bayesian Computing with Tidy Data

A flexible framework for Approximate Bayesian Computation (ABC) that integrates with the tidyverse. Define simulation models and summary statistics as standard R functions, use 'dist_fns' to represent prior and posterior distributions, and perform inference via rejection sampling, Sequential Monte Carlo (SMC), or Adaptive ABC. The package provides tools for diagnostics, visualization, and convergence assessment, enabling reproducible Bayesian inference for complex models with intractable likelihoods.

Authors:Robert Challen [aut, cre]

tidyabc_0.0.1.tar.gz
tidyabc_0.0.1.zip(r-4.7)tidyabc_0.0.1.zip(r-4.6)tidyabc_0.0.1.zip(r-4.5)
tidyabc_0.0.1.tgz(r-4.6-any)tidyabc_0.0.1.tgz(r-4.5-any)
tidyabc_0.0.1.tar.gz(r-4.7-any)tidyabc_0.0.1.tar.gz(r-4.6-any)
tidyabc_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
tidyabc/json (API)
NEWS

# Install 'tidyabc' in R:
install.packages('tidyabc', repos = c('https://ai4ci.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ai4ci/tidyabc/issues

Pkgdown/docs site:https://ai4ci.github.io

Datasets:

On CRAN:

Conda:

4.40 score 25 scripts 101 exports 59 dependencies

Last updated from:6d673cfed3 (on 0.0.1). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK212
source / vignettesOK307
linux-release-x86_64OK206
macos-release-arm64OK136
macos-oldrel-arm64OK117
windows-develOK196
windows-releaseOK159
windows-oldrelOK172
wasm-releaseOK125

Exports:%>%abc_adaptiveabc_rejectionabc_smcas.abc_prioras.dist_fnsas.dist_fns_listas.link_fnsas.link_fns_listcalculate_rmsecalculate_wassersteindbeta2dcgammadefault_termination_fndgamma2dist_fnsdlnorm2dlogitnormdlogitnorm2dnbinom2dnulldwedgeempiricalempirical_cdfempirical_dataexample_adaptive_fitexample_obsexample_obsdataexample_priors_listexample_rejection_fitexample_scorer_fnexample_sim_fnexample_smc_fitexample_truthfixed_wave_termination_fnis.abc_prioris.dist_fnsis.dist_fns_listis.link_fnsis.link_fns_listkurtosislink_fnsmap_dist_fnsmap_link_fnsmap2_dist_fnsmap2_link_fnsmixturepbeta2pcgammapgamma2plnorm2plogitnormplogitnorm2plot_convergenceplot_correlationsplot_evolutionplot_simulationspmap_dist_fnspmap_link_fnspnbinom2pnullposterior_distance_metricsposterior_fit_analyticalposterior_fit_empiricalposterior_resampleposterior_summarisepriorspwedgeqbeta2qcgammaqgamma2qlnorm2qlogitnormqlogitnorm2qnbinom2qnullqwedgerbernrbeta2rcategoricalrcgammarexpgrowthrexpgrowthI0rgamma2rlnorm2rlogitnormrlogitnorm2rnbinom2rnullrwedgeskewtest_simulationtidy.abc_fittransformtruncatewasserstein_calculatorwbw.nrdwidenwmeanwquantilewsd

Dependencies:base64enccarrierclicodetoolscpp11crayondigestdplyrevaluatefarverfitdistrplusfurrrfuturegenericsggplot2globalsgluegtablehighrisobandjsonliteknitrlabelinglatticelifecyclelistenvlobstrlocfitmagrittrMASSMatrixmvtnormparallellypatchworkpillarpkgconfigprettyunitspurrrR6raggRColorBrewerrlangS7scalessignalstringistringrsurvivalsystemfontstextshapingtibbletidyrtidyselectutf8vctrsviridisLitewithrxfunyaml

ABC Adaptive

Rendered fromabc-adaptive.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-11-24
Started: 2025-11-17

ABC Sequential Monte-Carlo

Rendered fromabc-smc.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-11-20
Started: 2025-11-17

Getting started with tidyabc

Rendered fromtidyabc.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-11-24
Started: 2025-11-06

Simulation, scoring and convergence functions

Rendered fromscorer-functions.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-11-20
Started: 2025-11-17

Statistical distributions in tidyabc

Rendered froms3-distributions.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-11-24
Started: 2025-11-17

Readme and manuals

Help Manual

Help pageTopics
Perform ABC sequential adaptive fittingabc_adaptive
'abc_fit' S3 classabc_fit format.abc_fit new_abc_fit plot.abc_fit print.abc_fit summary.abc_fit tidy.abc_fit
'abc_prior' S3 classabc_prior as.abc_prior format.abc_prior is.abc_prior new_abc_prior plot.abc_prior print.abc_prior
Perfom simple ABC rejection algorithmabc_rejection
Perform ABC sequential Monte Carlo fittingabc_smc
Create a 'dist_fns' S3 objectas.dist_fns as.dist_fns.character as.dist_fns.fitdist as.dist_fns.function
Create a 'link_fns' S3 objectas.link_fns as.link_fns.character as.link_fns.dist_fns as.link_fns.family as.link_fns.numeric
Concatenate a 'dist_fns' S3 object or 'dist_fns_list'sc.dist_fns
Concatenate a 'link_fns' S3 object or 'link_fns_list'sc.link_fns
Generate a function to calculate a Root Mean Squared Error (RMSE)calculate_rmse
Calculate a Wasserstein distancecalculate_wasserstein
The Beta Distributiondbeta2
Density: gamma distribution constrained to have mean > sddcgamma
Set up default convergence criteria for SMC and adaptive ABCdefault_termination_fn
The Gamma Distributiondgamma2
Create an empty 'dist_fns_list'dist_fns
The Log Normal Distributiondlnorm2
Logit-normal distributiondlogitnorm
Logit-normal distributiondlogitnorm2
The Negative Binomial Distributiondnbinom2
Null distributions always returns NAdnull
Wedge distributiondwedge
Fit a piecewise logit transformed linear model to cumulative dataempirical
Fit a piecewise logit transformed linear model to a CDFempirical_cdf
Fit a piecewise logit transformed linear model to weighted dataempirical_data
Run the SMC or adaptive algorithm for a set number of wavesfixed_wave_termination_fn
Format a 'dist_fns' S3 objectformat.dist_fns
Format a 'link_fns' S3 objectformat.link_fns
Check if this is a 'dist_fns' S3 objectis.dist_fns
Check if this is a 'dist_fns_list' S3 objectis.dist_fns_list
Check if this is a 'link_fns' S3 objectis.link_fns
Check if this is a 'link_fns_list' S3 objectis.link_fns_list
Calculate the excess kurtosis of a set of datakurtosis
Create an empty 'link_fns_list'link_fns
Apply a function to each element of a vector returning a 'dist_fns_list'map_dist_fns
Apply a function to each element of a vector returning a 'link_fns_list'map_link_fns
Map over two inputs returning a 'dist_fns_list'map2_dist_fns
Map over two inputs returning a 'link_fns_list'map2_link_fns
Construct a mixture distributionmixture
The Beta Distributionpbeta2
Cumulative probability: gamma distribution constrained to have mean > sdpcgamma
The Gamma Distributionpgamma2
The Log Normal Distributionplnorm2
Logit-normal distributionplogitnorm
Logit-normal distributionplogitnorm2
Plot convergence metrics by wave for SMC and adaptive ABCplot_convergence
A parameter posterior correlation plotplot_correlations
Plot the evolution of the density function by wave for SMC and adaptive ABCplot_evolution
Spaghetti plot of resampled posterior fitsplot_simulations
Plot a 'dist_fns' S3 objectplot.dist_fns
Plot a 'dist_fns_list' S3 objectplot.dist_fns_list
Map over multiple inputs returning a 'dist_fns_list'pmap_dist_fns
Map over multiple inputs returning a 'link_fns_list'pmap_link_fns
The Negative Binomial Distributionpnbinom2
Null distributions always returns NApnull
Generate a set of metrics from component scoresposterior_distance_metrics
Fit analytical distribution to posterior samples for generating more wavesposterior_fit_analytical
Fit empirical distribution to posterior samples for generating more wavesposterior_fit_empirical
Generate a set of samples from selected posteriorsposterior_resample
Calculate a basket of summaries from a weighted list of posterior samplesposterior_summarise
Construct a set of priorspriors
Wedge distributionpwedge
The Beta Distributionqbeta2
Quantile: gamma distribution constrained to have mean > sdqcgamma
The Gamma Distributionqgamma2
The Log Normal Distributionqlnorm2
Logit-normal distributionqlogitnorm
Logit-normal distributionqlogitnorm2
The Negative Binomial Distributionqnbinom2
Null distributions always returns NAqnull
Wedge distributionqwedge
A random Bernoulli sample as a logical valuerbern
The Beta Distributionrbeta2
Sampling from the multinomial equivalent of the Bernoulli distributionrcategorical
Sampling: gamma distribution constrained to have mean > sdrcgamma
Randomly sample incident times in an exponentially growing processrexpgrowth
Randomly sample incident times in an exponentially growing process with initial case loadrexpgrowthI0
The Gamma Distributionrgamma2
The Log Normal Distributionrlnorm2
Logit-normal distributionrlogitnorm
Logit-normal distributionrlogitnorm2
The Negative Binomial Distributionrnbinom2
Null distributions always returns NArnull
Wedge distributionrwedge
The 'sim_outbreak' datasetsim_outbreak
Calculate the skew of a set of dataskew
Run the simulation for one set of parameterstest_simulation
Generate a distribution from a link transform of anothertransform
Generate a distribution from a truncation of anothertruncate
Generate a function to calculate a wasserstein distancewasserstein_calculator
Weighted bandwidth selectorwbw.nrd
Wedge distributionwedge
Increase the dispersion of a distributionwiden
Weighted meanwmean
Quantile from weighted data with link function supportwquantile
Weighted standard deviationwsd