Fast, Easy, and Visual Bayesian Inference

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Documentation for package ‘causact’ version 0.5.5

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%>% The magrittr pipe
addPriorGroups Group together latent parameters by prior distribution.
baseballData Dataframe of 12,145 observations of baseball games in 2010 - 2014
beachLocDF Dataframe where each row represents data about one of the 26 mile markers (fake) from mile 0 to mile 2.5 along the Ocean City, MD beach/boardwalk.
bernoulli probability distributions
beta probability distributions
binomial probability distributions
carModelDF Dataframe of 1000 (fake) observations of whether certain car buyers were willing to get information on a credit card speciailizing in rewards for adventure travellers.
categorical probability distributions
cauchy probability distributions
check_r_causact_env Check if 'r-causact' Conda environment exists
chimpanzeesDF Data from behavior trials in a captive group of chimpanzees, housed in Lousiana. From Silk et al. 2005. Nature 437:1357-1359 and further popularized in McElreath, Richard. Statistical rethinking: A Bayesian course with examples in R and Stan. CRC press, 2020. Experiment
chi_squared probability distributions
corruptDF Dataframe of 174 observations where information on the human developmet index (HDI) and the corruption perceptions index (CPI) both exist. Each observation is a country.
dagp_plot Plot posterior distribution from dataframe of posterior draws.
dag_create Create a graph object for drawing a DAG.
dag_diagrammer Convert graph to Diagrammer object for visualization
dag_dim Add dimension information to 'causact_graph'
dag_edge Add edge (or edges) between nodes
dag_greta Generate a representative sample of the posterior distribution
dag_merge Merge two non-intersecting 'causact_graph' objects
dag_node Add a node to an existing 'causact_graph' object
dag_numpyro Generate a representative sample of the posterior distribution
dag_plate Create a plate representation for repeated nodes.
dag_render Render the graph as an htmlwidget
delivDF 117,790 line items associated with 23,339 shipments.
dirichlet probability distributions
distributions probability distributions
exponential probability distributions
gamma probability distributions
gymDF Dataframe of 44 observations of free crossfit classes data Each observation indicates how many students that participated in the free month of crossfit signed up for the monthly membership afterwards
houseDF Dataframe of 1,460 observations of home sales in Ames, Iowa. Known as The Ames Housing dataset, it was compiled by Dean De Cock for use in data science education. Each observation is a home sale. See 'houseDFDescr' for more info.
houseDFDescr Dataframe of 523 descriptions of data values from "The Ames Housing dataset", compiled by Dean De Cock for use in data science education. Each observation is a possible value from a variable in the 'houseDF' dataset.
install_causact_deps Install causact's python dependencies like numpyro, arviz, and xarray.
inverse_gamma probability distributions
laplace probability distributions
lkj_correlation probability distributions
logistic probability distributions
lognormal probability distributions
meaningfulLabels Store meaningful parameter labels
multinomial probability distributions
multivariate_normal probability distributions
negative_binomial probability distributions
normal probability distributions
pareto probability distributions
poisson probability distributions
prodLineDF Product line and product category assignments for 12,026 partID's.
rbern The Bernoulli Distribution
schoolsDF This example, often referred to as 8-schools, was popularized by its inclusion in Bayesian Data Analysis (Gelman, Carlin, & Rubin 1997).
setDirectedGraphTheme Set DiagrammeR defaults for graphical models
student probability distributions
ticketsDF Dataframe of 55,167 observations of the number of tickets written by NYC precincts each day Data modified from which originally sourced data from
totalBeachgoersRepSample A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days. Think of this representative sample as coming from either a prior or posterior distribution. An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at
uniform probability distributions
weibull probability distributions