Packaged: 2022-07-16 14:14:11 UTC; cpsievert

Author: Ramnath Vaidyanathan, Joe Cheng, JJ Allaire, Yihui Xie,
Description: A framework for creating HTML widgets that render in various
contexts including the R console, R Markdown documents, and Shiny
Imports: htmltools (>= 0.2.6), RJSONIO, yaml
Packaged: 2014-12-09 12:55:41 UTC; jjallaire

person("Ramnath", "Vaidyanathan", role = c("aut", "cph")),
person("Joe", "Cheng", role = c("aut")),
person("Kenton", "Russell", role = c("aut", "cph")),
contexts including the R console, 'R Markdown' documents, and 'Shiny'
Imports: htmltools (>= 0.2.6), jsonlite (>= 0.9.16), yaml
Packaged: 2015-05-18 18:23:26 UTC; jjallaire
Author: Ramnath Vaidyanathan [aut, cph],

Packaged: 2015-06-21 11:14:18 UTC; jjallaire

Imports: htmltools (>= 0.3), jsonlite (>= 0.9.16), yaml
Packaged: 2016-02-25 13:47:15 UTC; jjallaire

Packaged: 2016-08-02 00:24:06 UTC; jjallaire

Packaged: 2016-11-09 20:41:12 UTC; jjallaire

Packaged: 2017-07-10 10:49:02 UTC; jjallaire

Packaged: 2018-01-19 21:28:23 UTC; jjallaire

Imports: grDevices, htmltools (>= 0.3), jsonlite (>= 0.9.16), yaml
Packaged: 2018-04-18 14:06:07 UTC; jjallaire

person("Joe", "Cheng", role = c("aut"), email = "joe@rstudio.com"),
Packaged: 2020-10-03 15:06:05 UTC; cpsievert

person("Ellis", "Hughes", role = c("ctb")),
Suggests: knitr (>= 1.8), rmarkdown, testthat
Packaged: 2020-12-10 16:43:45 UTC; cpsievert

Packaged: 2021-09-08 13:21:35 UTC; cpsievert

Title: Historical Tree Ensembles for Longitudinal Data
Description: Historical regression trees are an extension of standard trees,
producing a non-parametric estimate of how the response depends on
all of its prior realizations as well as that of any time-varying predictor
variables. The method applies equally to regularly as well as irregularly
sampled data. The package implements random forest and boosting ensembles
based on historical regression trees, suitable for longitudinal data.

Standard error estimation and Z-score variable importance is also implemented.

Author: Rob J Hyndman, Roman A Ahmed, and Han Lin Shang
Description: Methods for analysing and forecasting hierarchical time