Browse Items (17 total)

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…

The consideration of a patient’s treatment preference may be essential in determining
how a patient will respond to a particular treatment. While traditional clinical trials are
unable to capture these effects, the two-stage randomized preference…

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…

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…

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…

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,…

Disease spreading simulations are traditionally performed using coupled differential
equations. However, in the setting of metapopulations, most of the solutions provided by
this method do not account for the dynamic topography of subpopulations.…

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…

A large number of statistical decision problems in the social sciences and beyond can
be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard
to develop and evaluate policies that tackle these types of problems, and…