Package: grizbayr 1.3.5

grizbayr: Bayesian Inference for A|B and Bandit Marketing Tests

Uses simple Bayesian conjugate prior update rules to calculate the win probability of each option, value remaining in the test, and percent lift over the baseline for various marketing objectives. References: Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>. Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.

Authors:Ryan Angi

grizbayr_1.3.5.tar.gz
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grizbayr.pdf |grizbayr.html✨
grizbayr/json (API)
NEWS

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

Bug tracker:https://github.com/rangi513/grizbayr/issues

On CRAN:

Conda:

4.70 score 10 stars 3 scripts 317 downloads 16 exports 21 dependencies

Last updated 1 years agofrom:8102c9f759. Checks:3 OK, 6 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 10 2025
R-4.5-winNOTEMar 10 2025
R-4.5-macNOTEMar 10 2025
R-4.5-linuxNOTEMar 10 2025
R-4.4-winNOTEMar 10 2025
R-4.4-macNOTEMar 10 2025
R-4.4-linuxNOTEMar 10 2025
R-4.3-winOKMar 10 2025
R-4.3-macOKMar 10 2025

Exports:estimate_all_valuesestimate_liftestimate_lift_vs_baselineestimate_lossestimate_value_remainingestimate_win_probestimate_win_prob_given_posteriorestimate_win_prob_vs_baselineestimate_win_prob_vs_baseline_given_posteriorfind_best_optionrdirichletsample_from_posteriorupdate_betaupdate_dirichletupdate_gammavalidate_input_df

Dependencies:clicpp11dplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Intro Examples to grizbayr

Rendered fromintro.Rmdusingknitr::rmarkdownon Mar 10 2025.

Last update: 2020-08-05
Started: 2020-04-23

Readme and manuals

Help Manual

Help pageTopics
Calculate Multi Rev Per Sessioncalculate_multi_rev_per_session
Calculate Total CMcalculate_total_cm
Estimate All Valuesestimate_all_values
Estimate Lift Distributionestimate_lift
Estimate Lift vs Baselineestimate_lift_vs_baseline
Estimate Lossestimate_loss
Estimate Value Remainingestimate_value_remaining
Estimate Win Probabilityestimate_win_prob
Estimate Win Probability Given Posterior Distributionestimate_win_prob_given_posterior
Estimate Win Probability vs. Baselineestimate_win_prob_vs_baseline
Estimate Win Probability vs. Baseline Given Posteriorestimate_win_prob_vs_baseline_given_posterior
Find Best Optionfind_best_option
Impute Missing Optionsimpute_missing_options
Is Prior Validis_prior_valid
Is Winner Maxis_winner_max
Random Dirichletrdirichlet
Sample CM Per Clicksample_cm_per_click
Sample Conversion Ratesample_conv_rate
Sample Cost Per Activation (CPA)sample_cpa
Sample Cost Per Clicksample_cpc
Sample Click Through Ratesample_ctr
Sample From Posteriorsample_from_posterior
Sample Multiple Revenue Per Sessionsample_multi_rev_per_session
Sample Page Views Per Session (Visit)sample_page_views_per_session
Sample Response Ratesample_response_rate
Sample Rev Per Sessionsample_rev_per_session
Sample Session Durationsample_session_duration
Sample Total CM (Given Impression Count)sample_total_cm
Update Betaupdate_beta
Update Dirichlet Distributionupdate_dirichlet
Update Gammaupdate_gamma
Validate Data Valuesvalidate_data_values
Validate Input Columnvalidate_input_column
Validate Input DataFramevalidate_input_df
Validate Posterior Samples Dataframevalidate_posterior_samples
Validate Priorsvalidate_priors
Validate With Respect To Optionvalidate_wrt_option