Fast, Easy, and Visual Bayesian Inference

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Documentation for package ‘causact’ version 0.5.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

%>%	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 varaible 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 https://github.com/stan-dev/stancon_talks/tree/master/2018/Contributed-Talks/01_auerbach which originally sourced data from https://opendata.cityofnewyork.us/
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 https://www.causact.com/.
uniform	probability distributions
weibull	probability distributions