Browse Items (8208 total)

In this study, we present a new module built for users interested in a programming
language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE)
distribution. The module is an extension to the open-source program JAGS by…

ABCpy is a highly modular scientific library for approximate Bayesian computation
(ABC) written in Python. The main contribution of this paper is to document a software
engineering effort that enables domain scientists to easily apply ABC to their…

Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model
estimation is complicated by the fact that we typically have multiple…

Item response theory (IRT) is widely applied in the human sciences to model persons’
responses on a set of items measuring one or more latent constructs. While several
R packages have been developed that implement IRT models, they tend to be…

Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible regression model specifications
are needed as data sizes and available information are steadily increasing,…

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model
specification and the ability to program model-generic algorithms. Specifically,…

The INLA package provides a tool for computationally efficient Bayesian modeling
and inference for various widely used models, more formally the class of latent Gaussian
models. It is a non-sampling based framework which provides approximate…

In this summary we introduce the papers published in the special issue on Bayesian
statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian
inference on different topics such as general packages for hierarchical linear…

Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES
provides the standard least squares estimators and the more efficient…

Statistical analyses of directional or angular data have applications in a variety of
fields, such as geology, meteorology and bioinformatics. There is substantial literature on
descriptive and inferential techniques for univariate angular data,…