knitr, ggplot2, rmarkdown, testthat, dplyr, tidyr,
ggbeeswarm, covr

Cramer von Mises Tests for Discrete or Grouped Distributions
c(person("Shaun Zheng", "Sun", email="Shaun.Sun@ufv.ca", role = c("aut", "cre")),
person("Dillon", "Duncan", email="dillon.duncan@student.ufv.ca", role = "aut"))
Implements Cramer-von Mises Statistics for testing fit to (1) fully specified discrete distributions as described in Choulakian, Lockhart and Stephens (1994) <doi:10.2307/3315828> (2) discrete distributions with unknown parameters that must be estimated from the sample data, see Spinelli & Stephens (1997) <doi:10.2307/3315735> and Lockhart, Spinelli and Stephens (2007) <doi:10.1002/cjs.5550350111> (3) grouped continuous distributions with Unknown Parameters, see Spinelli (2001) <doi:10.2307/3316040>. Maximum likelihood estimation (MLE) is used to estimate the parameters. The package computes the Cramer-von Mises Statistics, Anderson-Darling Statistics and the Watson-Stephens Statistics and their p-values.
Shaun Zheng Sun [aut, cre],
Dillon Duncan [aut]

Romain Azais, Sandie Ferrigno and Marie-Jose Martinez
It is devoted to Cramer-von Mises goodness-of-fit tests.
It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

c(
person("Ludvig Renbo", "Olsen", email = "r-pkgs@ludvigolsen.dk", role = c("aut", "cre")),
person("Benjamin Hugh", "Zachariae", role="aut")
)
Run cross-validation of one or multiple regression models and get relevant evaluation
metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation.
Currently supports Gaussian and binomial regression.
Described in chp. 5 of Jeyaraman, B., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).
knitr, groupdata2 (>= 1.1), e1071, rmarkdown, testthat, AUC,
furrr, ModelMetrics
Ludvig Renbo Olsen [aut, cre],
Benjamin Hugh Zachariae [aut]

Run cross-validation of one or multiple regression models and get relevant evaluation
metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation.
Currently supports Gaussian and binomial regression.
Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).
knitr, groupdata2 (>= 1.1.1), e1071, rmarkdown, testthat,
AUC, furrr, ModelMetrics