Browse Items (20 total)

v100i11.pdf
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…

v100i10.pdf
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…

v100i09 (1).pdf
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…

v100i08.pdf
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…

v100i07.pdf
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…

v100i06.pdf
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…

v100i05.pdf
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…

v100i03.pdf
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,…

v100i02.pdf
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…

v100i01.pdf
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…
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