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

c(person(given = "Jiena",
family = "McLellan",
role = c("aut", "cre"),
email = "jienagu90@gmail.com",
comment = c(ORCID = "0000-0002-5578-088X")),
person(given = "Michael",
family = "Condouris",
role = "ctb",
comment = c(ORCID = "0000-0002-8862-4250")))
Create a flip over style Flash Card with desired data frame for Shiny application.
Jiena McLellan [aut, cre] (<https://orcid.org/0000-0002-5578-088X>),
Michael Condouris [ctb] (<https://orcid.org/0000-0002-8862-4250>)

Implementation of optimal hierarchical clustering
code by Fionn Murtagh and R development team, modifications and
packaging by Peter Langfelder

code by Fionn Murtagh, packaging by Peter Langfelder

Fast implementation of hierarchical clustering

Shed Light on Black Box Machine Learning Models
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
dependence (Friedman J. H. (2001) <doi.org/10.1214/aos/1013203451>),
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.
stats, utils, dplyr, tidyr, rlang, ggplot2, ggpubr,
MetricsWeighted (>= 0.2.0)
knitr, lubridate, ranger, xgboost, caret, moderndive

Shed light on black box machine learning models by
the help of model performance, permutation variable importance (Fisher
et al. (2018) <arxiv:1801.01489>), ICE profiles, partial dependence
(Friedman J. H. (2001) <doi:10.1214/aos/1013203451>), further effects
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
analyzed together.

Shed light on black box machine learning models by
the help of model performance, permutation variable importance (Fisher
et al. (2018) <arxiv:1801.01489>), ICE profiles, partial dependence
(Friedman J. H. (2001) <doi:10.1214/aos/1013203451>), accumulated
local effects (Apley D. W. (2016) <arXiv:1612.08468>), further effects
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
analyzed together.