Volume 100 Tahun 2021
Dublin Core
Title
Volume 100 Tahun 2021
Source
https://www.jstatsoft.org/issue/view/v100
Date
2021
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Collection Items
Software for Bayesian Statistics
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…
statistics. This special issue comprises 20 papers on Bayesian statistics and Bayesian
inference on different topics such as general packages for hierarchical linear…
New Frontiers in Bayesian Modeling Using the INLA Package in R
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…
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…
Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
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,…
specification and the ability to program model-generic algorithms. Specifically,…
Bayesian Item Response Modeling in R with brms and Stan
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…
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…
Efficient Bayesian Structural Equation Modeling in Stan
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…
estimation is complicated by the fact that we typically have multiple…
ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation
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…
(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…
pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution
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…
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…
qgam: Bayesian Nonparametric Quantile Regression Modeling in R
Generalized additive models (GAMs) are flexible non-linear regression models, which
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption…
can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R
package. While the GAM methods provided by mgcv are based on the assumption…
dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble
Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many
biological studies seek to understand non-deterministic patterns in the…
biological studies seek to understand non-deterministic patterns in the…
BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression
In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied…
high-dimensional genomic and other omics data, a problem that can be studied…