Browse Items (17 total)

The R package AssocTests provides some procedures which are commonly used in
genetic association studies. These procedures are population stratification correction
through eigenvectors, principal coordinates of clusterings, Tracy-Widom test,…

We present the R package BASS as a tool for nonparametric regression. The primary
focus of the package is fitting fully Bayesian adaptive spline surface (BASS) models and
performing global sensitivity analyses of these models. The BASS framework is…

The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed
(discrete and continuous). Probabilistic reasoning enables a user to absorb…

We introduce a new R package, BeSS, for solving the best subset selection problem
in linear, logistic and Cox’s proportional hazard (CoxPH) models. It utilizes a highly
efficient active set algorithm based on primal and dual variables, and supports…

This article describes the R package BOIN, which implements a recently developed
methodology for designing single-agent and drug-combination dose-finding clinical trials
using Bayesian optimal interval designs (Liu and Yuan 2015; Yuan, Hess,…

The R add-on package FDboost is a flexible toolbox for the estimation of functional
regression models by model-based boosting. It provides the possibility to fit regression
models for scalar and functional response with effects of scalar as well as…

Although a very rich list of classes of space-time covariance functions exists, specific
tools for selecting the appropriate class for a given data set are needed. Thus, the main
topic of this paper is to present the new R package, covatest, which…

Subset identification methods are used to select the subset of a covariate space over
which the conditional distribution of a response has certain properties – for example,
identifying types of patients whose conditional treatment effect is…

CVXR is an R package that provides an object-oriented modeling language for convex
optimization, similar to CVX, CVXPY, YALMIP, and Convex.jl. It allows the user to
formulate convex optimization problems in a natural mathematical syntax rather…

Empirical Bayes inference assumes an unknown prior density g(θ) has yielded (unobservables) Θ1, Θ2, . . . , ΘN , and each Θi produces an independent observation Xi from
pi(Xi
|Θi). The marginal density fi(Xi) is a convolution of the prior g and…