Browse Items (9465 total)

This paper introduces the usage and performance of the R package tlrmvnmvt, aimed
at computing high-dimensional multivariate normal and Student-t probabilities. The
package implements the tile-low-rank methods with block reordering and the…

This paper introduces the R package FKSUM, which offers fast and exact evaluation of
univariate kernel smoothers. The main kernel computations are implemented in C++, and
are wrapped in simple, intuitive and versatile R functions. The fast kernel…

Density estimation and inference methods are widely used in empirical work. When
the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their…

The poolr package provides an implementation of a variety of methods for pooling
(i.e., combining) p values, including Fisher’s method, Stouffer’s method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett’s method.…

This article introduces the R package BayesCTDesign for two-arm randomized Bayesian
trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The…

Missing data occur in many types of studies and typically complicate the analysis.
Multiple imputation, either using joint modeling or the more flexible fully conditional
specification approach, are popular and work well in standard settings. In…

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for…

There have been considerable methodological developments of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development
is due to the flexibility of the Bayes factor for testing multiple…

Booming in business and a staple analysis in medical trials, the A/B test assesses
the effect of an intervention or treatment by comparing its success rate with that of a
control condition. Across many practical applications, it is desirable that…

We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we
consider the case when S matrices are available, each describing the…