Description: Colored terminal output on terminals that support 'ANSI'
color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
color support is automatically detected. Colors and highlighting can
be combined and nested. New styles can also be created easily.
This package was inspired by the 'chalk' 'JavaScript' project.
Imports: memoise, grDevices, methods, utils
Packaged: 2015-07-13 00:29:52 UTC; gaborcsardi

Collate: 'ansi-256.r' 'combine.r' 'string.r' 'utils.r'
Packaged: 2016-06-28 17:18:56 UTC; gaborcsardi

Suggests: mockery, rstudioapi, testthat, withr
Packaged: 2017-09-15 18:14:04 UTC; gaborcsardi

'disposable.r' 'has_ansi.r' 'has_color.r' 'link.R' 'styles.r'
'machinery.r' 'parts.r' 'print.r' 'rematch2.R' 'style-var.r'
Packaged: 2021-01-30 17:41:55 UTC; gaborcsardi

Packaged: 2021-02-08 18:57:04 UTC; gaborcsardi

Packaged: 2021-10-26 12:16:51 UTC; gaborcsardi

'link.R' 'styles.r' 'machinery.r' 'parts.r' 'print.r'
Packaged: 2022-02-10 17:53:01 UTC; gaborcsardi

Packaged: 2022-03-26 16:18:07 UTC; gaborcsardi

Description: The crayon package is now superseded. Please use the 'cli' package
Colored terminal output on terminals that support 'ANSI'
'has_color.r' 'link.R' 'styles.r' 'machinery.r' 'parts.r'
Packaged: 2022-09-29 06:24:10 UTC; gaborcsardi

Title: Categorical Randomized Block Data Analysis
Description: Implements a statistical test for comparing bar plots or
histograms of categorical data derived from a randomized block

Description: Implements a statistical test for comparing bar plots or histograms of categorical data derived from a randomized block repeated measures layout.

Title: Censored Regression with Conditional Heteroscedasticity
Authors@R: c(person(given = "Jakob", family = "Messner", role =
c("aut", "cre"), email = "Jakob.Messner@uibk.ac.at"),
Description: Different approaches to censored regression with conditional
heteroscedasticity are provided. First, continuous distributions can be used
for the (right and/or left censored) response with separate linear predictors
for the mean and variance. Second, cumulative link models for ordinal data
(obtained by interval-censoring continuous data) can be employed for
heteroscedastic extended logistic regression (HXLR). In the latter type of
models, the intercepts depend on the thresholds that define the intervals.