Browse Items (9465 total)

Optimization plays an important role in many methods routinely used in statistics,
machine learning and data science. Often, implementations of these methods rely on
highly specialized optimization algorithms, designed to be only applicable within…

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…

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,…

We present the R package PResiduals for residual analysis using the probability-scale
residual. This residual is well defined for a wide variety of outcome types and models, including some settings where other popular residuals are not applicable.…

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 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…

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…

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…

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…

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.…