Browse Items (9465 total)

The simsurv R package allows users to simulate survival (i.e., time-to-event) data from
standard parametric distributions (exponential, Weibull, and Gompertz), two-component
mixture distributions, or a user-defined hazard function. Baseline…

The R package microsynth has been developed for implementation of the synthetic
control methodology for comparative case studies involving micro- or meso-level data.
The methodology implemented within microsynth is designed to assess the efficacy…

In this article, we introduce the BART R package which is an acronym for Bayesian additive regression trees. BART is a Bayesian nonparametric, machine learning, ensemble
predictive modeling method for continuous, binary, categorical and…

The R package vsgoftest performs goodness-of-fit (GOF) tests, based on Shannon
entropy and Kullback-Leibler divergence, developed by Vasicek (1976) and Song (2002),
of various classical families of distributions. The so-called Vasicek-Song (VS)…

This paper introduces the R package ordinalCont, which implements an ordinal regression framework for response variables which are recorded on a visual analogue scale
(VAS). This scale is used when recording subjects’ perception of an intangible…

Traditional tools and software for social network analysis are seldom scalable and/or
fast. This paper provides an overview of an R package called fastnet, a tool for scaling
and speeding up the simulation and analysis of large-scale social…

Wilcoxon rank-based tests are distribution-free alternatives to the popular two-sample
and paired t tests. For independent data, they are available in several R packages such
as stats and coin. For clustered data, in spite of the recent…

This article introduces the GNAR package, which fits, predicts, and simulates from
a powerful new class of generalized network autoregressive processes. Such processes
consist of a multivariate time series along with a real, or inferred, network…

The LocalControl R package implements novel approaches to address biases and confounding when comparing treatments or exposures in observational studies of outcomes.
While designed and appropriate for use in comparative safety and effectiveness…

Discrete choice experiments are widely used in a broad area of research fields to capture
the preference structure of respondents. The design of such experiments will determine
to a large extent the accuracy with which the preference parameters can…