tidyr, purrr, dplyr, magrittr, RColorBrewer
Suggests: TH.data, ggforce, webshot, ztable, moonBook, maps, gcookbook,
Packaged: 2020-10-06 06:38:43 UTC; cardiomoon

Title: 'ggplot2' Friendly Isotonic or Monotonic Regression Curves
Authors@R: person("Komala Sheshachala", "Srikanth", email = "sri.teach@gmail.com", role = c("aut", "cre"))
Description: Provides stat_isotonic() to add weighted univariate isotonic regression curves.
Packaged: 2022-05-23 16:29:35 UTC; dattachidambara
Author: Komala Sheshachala Srikanth [aut, cre]

person("Claus O.", "Wilke", , "wilke@austin.utexas.edu", c("cre", "aut")),
Description: Joyplots provide a convenient way of visualizing changes in distributions over
time or space. This package enables the creation of such plots in 'ggplot2'.
Collate: 'data.R' 'ggjoy.R' 'geoms.R' 'stats.R' 'theme.R'

Suggests: knitr, ggplot2movies, cowplot, forcats, tidyverse, viridis
Collate: 'data.R' 'ggjoy.R' 'geoms.R' 'geoms-gradient.R' 'stats.R'

person("RStudio", role = c("cph"), comment = "Copyright for ggplot2 code copied to ggjoy."))
Collate: 'data.R' 'ggjoy.R' 'geoms.R' 'geoms-gradient.R'
RStudio [cph] (Copyright for ggplot2 code copied to ggjoy.)

person("RStudio", role = c("cph"), comment = "Copyright for ggplot2 code copied to ggjoy"))
Description: Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.
Suggests: covr, gridExtra, ggplot2movies, forcats, knitr, tidyverse,
RStudio [cph] (Copyright for ggplot2 code copied to ggjoy)

person("Claus O.", "Wilke", , "wilke@austin.utexas.edu", c("cre", "aut")))

Title: Group Lasso Penalized Learning Using A Unified BMD Algorithm
Description: This package implements a unified algorithm,
blockwise-majorization-decent (BMD), for efficiently computing
the solution paths of the group-lasso penalized least squares,
logistic regression, Huberized SVM and squared SVM.

Description: This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.