Suggests: farff, knitr, rmarkdown, rpart, testthat, xgboost

magrittr, mlr, Metrics, plotly, RColorBrewer, reticulate,
rlang, rpart.plot, rsample, sensitivity, smoof, SPOT, stats,

Imports: benchmarkme, callr, emoa, ggsci, graphics, grDevices,
jsonlite, keras, mlr, plotly, RColorBrewer, rpart.plot,
sensitivity, SimInf, smoof, SPOT, stats, utils
Suggests: babsim.hospital, knitr, rmarkdown, testthat

It provides tools for interfacing 'mlr', for performing Markov chain simulations,
Imports: benchmarkme, callr, emoa, ggplot2, ggsci, graphics, grDevices,
MASS, plotly, RColorBrewer, rpart.plot, sensitivity, SimInf,

jsonlite, keras, magrittr, mlr, OpenML, plotly, RColorBrewer,
reticulate, rpart.plot, sensitivity, smoof, SPOT, stats,

Title: Scatter Plots Over-Viewed Using Summary Ellipses
Description: Summary ellipses superimposed on a scatter plot contain all bi-variate summary
statistics for regression analysis. Furthermore, the outer ellipse flags potential
outliers. Multiple groups can be compared in terms of centers and spreads as illustrated

Title: Combining Different Spatial Datasets in Cancer Risk Estimation
Description: We propose a novel two-step procedure to combine epidemiological
data obtained from diverse sources with the aim to quantify risk factors
affecting the probability that an individual develops certain disease such as
cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical

Authors@R: c(person("Steven", "Xu", email = "sgxu@ncsu.edu", role = c("aut","cre")),
person("Reetam", "Majumder", email = "rmajumd3@ncsu.edu", role = c("aut")),
person("Brian", "Reich", email = "bjreich@ncsu.edu", role = c("ctb")))
Description: Methods for flexible estimation of conditional density and quantile function, as well as model agnostic tools for analyzing quantile covariate effect and variable importance. The estimation method implements the semi-parametric quantile regression model described in Xu and Reich (2021) <doi:10.1111/biom.13576>, and the model agnostic tools extend accumulative local effects (ALE) to quantile regression setting.
Imports: Rcpp (>= 1.0.8), stats, torch, splines2, ggplot2, loo,
progress, progressr, interp, RColorBrewer, yaImpute, coro (>=