Interactive Conditional Visualization for Supervised and
Unsupervised Models in Shiny
shiny, RColorBrewer, ggplot2,scales, cluster,
DendSer,methods,plyr, colorspace, gower
knitr, rmarkdown, hdrcde, scagnostics, keras, kernlab,
mclust, MASS, ks, mgcv, randomForest, parsnip, mlr, C50,
bartMachine, caret, e1071, gbm, glmnet, glmnetUtils, mlr3,
nnet, rpart, tree, testthat

Constructs a shiny app function with interactive displays for conditional visualization of models,
data and density functions. An extended version of package 'condvis'.
Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.
knitr, rmarkdown, hdrcde, scagnostics, keras, kernlab,
mclust, MASS, ks, mgcv, randomForest, parsnip, mlr, C50,
bartMachine, BART, caret, e1071, gbm, glmnet, glmnetUtils,
mlr3, nnet, rpart, tree, testthat

Primal or Dual Cone Projections with Routines for
Shape-restricted Regression
This package contains routines for cone projection and quadratic programming, as well as routines to do estimation and inference for constrained parametric regression and shape-restricted regression problems.

Primal or Dual Cone Projections with Routines for Constrained
Regression
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems.

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

c(person("Christopher", "Weld", role = c("aut", "cre"), email = "ceweld@email.wm.edu", comment = c(ORCID = "0000-0001-5902-9738")),
person("Lawrence", "Leemis", role = "aut"))
Plots the two-dimensional confidence region for probability distribution (Weibull or
inverse Gaussian) parameters corresponding to a user given dataset and level of significance.
The crplot() algorithm plots more points in areas of greater curvature to ensure a smooth
appearance throughout the confidence region boundary.  An alternative heuristic plots a specified
number of points at roughly uniform intervals along its boundary.  Both heuristics build upon the
radial profile log-likelihood ratio technique for plotting two-dimensional confidence regions given
by Jaeger (2016) <doi:10.1080/00031305.2016.1182946>.
Christopher Weld [aut, cre] (<https://orcid.org/0000-0001-5902-9738>),
Lawrence Leemis [aut]