Packaged: 2020-11-30 18:10:54 UTC; maomao-xingguo

Description: Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.

Description: Create a flip over style Flash Card with desired data frame for Shiny application.
Packaged: 2020-08-07 15:39:40 UTC; Jiena_Mclellan

Title: Implementation of optimal hierarchical clustering
Author: code by Fionn Murtagh and R development team, modifications and
Description: Implementation of optimal hierarchical clustering
Packaged: Fri Nov 13 17:13:56 2009; plangfelder

Author: code by Fionn Murtagh, packaging by Peter Langfelder
Packaged: Wed Feb  4 17:23:54 2009; plangfelder

Packaged: 2010-07-20 00:15:23 UTC; plangfelder

Packaged: 2011-02-06 19:43:44 UTC; plangfelder

Packaged: 2010-02-19 21:24:31 UTC; plangfelder

Description: Fast implementation of hierarchical clustering
Packaged: 2012-03-03 02:26:41 UTC; plangfelder

Packaged: 2012-08-21 17:18:30 UTC; plangfelder

Packaged: 2011-03-19 18:30:36 UTC; plangfelder

Title: Shed Light on Black Box Machine Learning Models
Description: Shed light on black box machine learning models by
the help of model performance, permutation variable importance (Fisher
et al. (2018) <arxiv.org/abs/1801.01489>), ICE profiles, partial
and further effects plots. All tools are implemented to work with case
weights and allow for stratified analysis. Furthermore, multiple
flashlights can be combined and analyzed together.
Imports: stats, utils, dplyr, tidyr, rlang, ggplot2, ggpubr,
Suggests: knitr, lubridate, ranger, xgboost, caret, moderndive

et al. (2018) <arxiv:1801.01489>), ICE profiles, partial dependence
plots, and variable contribution breakdown for single observations
(Gosiewska and Biecek (2019) <arxiv:1903.11420>). All tools are
implemented to work with case weights and allow for stratified
analysis. Furthermore, multiple flashlights can be combined and

local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects

plots, overall interaction strength, and variable contribution
breakdown for single observations (Gosiewska and Biecek (2019)
<arxiv:1903.11420>). All tools are implemented to work with case