Browse Items (17 total)

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…

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…

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…

Fractional factorial experiments often produce ambiguous results due to confounding
among the factors; as a consequence more than one model is consistent with the data.
Thus, the practical problem is how to choose additional runs in order to…

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.…

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…

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.…

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…

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…

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…