Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" <arXiv:	arXiv:2006.16194>,
Packaged: 2020-07-27 02:41:15 UTC; samuelthomas

Packaged: 2020-10-04 22:13:27 UTC; samuelthomas

Title: The H-Measure and Other Scalar Classification Performance
Description: Classification performance metrics that are derived from the ROC
curve of a classifier. The package includes the H-measure performance metric
which computes the minimum total misclassification cost, integrating over any
uncertainty about the relative misclassification costs, as per a user-defined
prior. It also offers a one-stop-shop for other scalar metrics of performance,
including sensitivity, specificity and many others, and also offers plotting
tools for ROC curves and related statistics.

Title: The H-measure and other scalar classification performance
Description: Scalar performance metrics, including the H-measure, based
on classification scores for several classifiers applied to the

Title: History Matching and Emulation Package
Authors@R: c(person("Andrew", "Iskauskas", email = "andrew.iskauskas@durham.ac.uk", comment = c(ORCID = "0000-0003-2825-3651"), role = c('aut', 'cre')))
Description: A set of objects and functions for Bayes Linear emulation and history matching.
Core functionality includes automated training of emulators to data, diagnostic functions
to ensure suitability, and a variety of proposal methods for generating 'waves' of points.
For details on the mathematical background, there are many papers available on the topic
(see references attached to function help files); for details of the functions in this package,
Suggests: spelling, knitr, rmarkdown, deSolve
Imports: purrr, stringr, tidyr, ggplot2, lhs, MASS, R6, viridis,
Author: Andrew Iskauskas [aut, cre] (<https://orcid.org/0000-0003-2825-3651>)

Suggests: spelling, knitr, rmarkdown, deSolve, testthat (>= 3.0.0),

mvtnorm, GGally, rlang, isoband, mclust, pdist

Title: High-Dimensional Mixed Graphical Models Estimation
Description: Provides weighted group lasso framework for high-dimensional mixed data graph estimation.
In the graph estimation stage, the graph structure is estimated by maximizing the conditional
likelihood of one variable given the rest. We focus on the conditional loglikelihood of each variable
and fit separate regressions to estimate the parameters, much in the spirit of the neighborhood
selection approach proposed by Meinshausen-Buhlmann for the Gaussian Graphical Model and by Ravikumar
for the Ising Model. Currently, the discrete variables can only take two values. In the future, method
for general discrete data and for visualizing the estimated graph will be added.
Imports: rgl, Matrix, glmnet, MASS, nat, binaryLogic, Rcpp, stats,