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
grizbayr_1.3.5.zip(r-4.7)grizbayr_1.3.5.zip(r-4.6)grizbayr_1.3.5.zip(r-4.5)
grizbayr_1.3.5.tgz(r-4.6-any)grizbayr_1.3.5.tgz(r-4.5-any)
grizbayr_1.3.5.tar.gz(r-4.7-any)grizbayr_1.3.5.tar.gz(r-4.6-any)
grizbayr_1.3.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
grizbayr/json (API)

# 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:

4.74 score 11 stars 4 scripts 273 downloads 16 exports 20 dependencies

Last updated from:8102c9f759. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	NOTE	194
source / vignettes	OK	307
linux-release-x86_64	NOTE	198
macos-release-arm64	NOTE	181
macos-oldrel-arm64	NOTE	164
windows-devel	NOTE	137
windows-release	NOTE	137
windows-oldrel	NOTE	143
wasm-release	OK	111

Exports:estimate_all_values estimate_lift estimate_lift_vs_baseline estimate_loss estimate_value_remaining estimate_win_prob estimate_win_prob_given_posterior estimate_win_prob_vs_baseline estimate_win_prob_vs_baseline_given_posterior find_best_option rdirichlet sample_from_posterior update_beta update_dirichlet update_gamma validate_input_df

Dependencies:cli cpp11 dplyr generics glue lifecycle magrittr pillar pkgconfig purrr R6 rlang stringi stringr tibble tidyr tidyselect utf8 vctrs withr

Intro Examples to grizbayr

Rendered fromintro.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate Multi Rev Per Session	calculate_multi_rev_per_session
Calculate Total CM	calculate_total_cm
Estimate All Values	estimate_all_values
Estimate Lift Distribution	estimate_lift
Estimate Lift vs Baseline	estimate_lift_vs_baseline
Estimate Loss	estimate_loss
Estimate Value Remaining	estimate_value_remaining
Estimate Win Probability	estimate_win_prob
Estimate Win Probability Given Posterior Distribution	estimate_win_prob_given_posterior
Estimate Win Probability vs. Baseline	estimate_win_prob_vs_baseline
Estimate Win Probability vs. Baseline Given Posterior	estimate_win_prob_vs_baseline_given_posterior
Find Best Option	find_best_option
Impute Missing Options	impute_missing_options
Is Prior Valid	is_prior_valid
Is Winner Max	is_winner_max
Random Dirichlet	rdirichlet
Sample CM Per Click	sample_cm_per_click
Sample Conversion Rate	sample_conv_rate
Sample Cost Per Activation (CPA)	sample_cpa
Sample Cost Per Click	sample_cpc
Sample Click Through Rate	sample_ctr
Sample From Posterior	sample_from_posterior
Sample Multiple Revenue Per Session	sample_multi_rev_per_session
Sample Page Views Per Session (Visit)	sample_page_views_per_session
Sample Response Rate	sample_response_rate
Sample Rev Per Session	sample_rev_per_session
Sample Session Duration	sample_session_duration
Sample Total CM (Given Impression Count)	sample_total_cm
Update Beta	update_beta
Update Dirichlet Distribution	update_dirichlet
Update Gamma	update_gamma
Validate Data Values	validate_data_values
Validate Input Column	validate_input_column
Validate Input DataFrame	validate_input_df
Validate Posterior Samples Dataframe	validate_posterior_samples
Validate Priors	validate_priors
Validate With Respect To Option	validate_wrt_option