Browse Items (8208 total)

Clustered covariances or clustered standard errors are very widely used to account for
correlated or clustered data, especially in economics, political sciences, and other social
sciences. They are employed to adjust the inference following…

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…

Data analysis projects invariably involve a series of steps such as reading, cleaning,
summarizing and plotting data, statistical analysis and reporting. To facilitate reproducible research, rather than employing a relatively ad-hoc point-and-click…

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…