Imports: bayestestR (>= 0.12.1), datasets, datawizard (>= 0.4.1),
insight (>= 0.17.1), parameters (>= 0.17.0), stats
Packaged: 2022-05-20 20:04:02 UTC; indrajeetpatil

Imports: bayestestR (>= 0.12.1), datasets, datawizard (>= 0.5.0),
insight (>= 0.18.0), parameters (>= 0.18.1), stats
Suggests: BayesFactor, poorman, energy, ggplot2, ggraph, gt, Hmisc,
knitr, lme4, mbend, polycor, ppcor, psych, rmarkdown, rmcorr,
rstanarm, see, testthat (>= 3.1.0), tidygraph, wdm, WRS2
Packaged: 2022-08-07 21:26:53 UTC; indrajeetpatil

Imports: bayestestR (>= 0.13.0), datasets, datawizard (>= 0.5.1),
insight (>= 0.18.4), parameters (>= 0.18.2), stats
Packaged: 2022-10-08 16:35:17 UTC; indrajeetpatil

Title: Speed Up Exploratory Data Analysis (EDA) with the Correlation
person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre"))
Speeds up exploratory data analysis (EDA)
by providing a succinct workflow and interactive visualization tools for understanding
which features have relationships to target (response). Uses binary correlation analysis
to determine relationship. Default correlation method is the Pearson method.
Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.
Imports: ggplot2, rlang, recipes, magrittr, plotly, tibble, dplyr,
tidyr, stats, utils, ggrepel, stringr, forcats, purrr, cli,
Suggests: scales, knitr, rmarkdown, covr, lubridate, testthat (>=

Imports: ggplot2, rlang, recipes, magrittr, plotly, tibble, dplyr (>=
1.0.0), tidyr (>= 1.0.0), stats, utils, ggrepel, stringr,

Author: Chris Rohlfs [aut], Gary Witt [aut,cre]
Description: Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.