Browse Items (8396 total)

This document introduces the R package BGVAR to estimate Bayesian global vector
autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian
treatment of GVARs allows to include large information sets by mitigating issues…

The highfrequency package for the R programming language provides functionality
for pre-processing financial high-frequency data, analyzing intraday stock returns, and
forecasting stock market volatility. For academics and practitioners alike, it…

synthACS is an R package that provides flexible tools for building synthetic microdatasets based on American Community Survey (ACS) base tables, allows data-extensibility
and enables to conduct spatial microsimulation modeling (SMSM) via simulated…

A large body of research has focused on theory and computation for variable selection
techniques for high dimensional data. There has been substantially less work in the
big “tall” data paradigm, where the number of variables may be large, but the…

The R package calculus implements C++-optimized functions for numerical and symbolic calculus, such as the Einstein summing convention, fast computation of the LeviCivita symbol and generalized Kronecker delta, Taylor series expansion, multivariate…

We introduce Pathogen.jl for simulation and inference of transmission network individual level models (TN-ILMs) of infectious disease spread in continuous time. TN-ILMs
can be used to jointly infer transmission networks, event times, and model…

Antimicrobial resistance is an increasing threat to global health. Evidence for this
trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and…

This paper describes the gretl function package ParMA, which provides Bayesian
model averaging (BMA) in generalized linear models. In order to overcome the lack
of analytical specification for many of the models covered, the package features an…

Recent advances in computational methods for intractable models have made network
data increasingly amenable to statistical analysis. Exponential random graph models
(ERGMs) emerged as one of the main families of models capable of capturing the…

The popularity of Bayesian statistical methods has increased dramatically in recent
years across many research areas and industrial applications. This is the result of a variety
of methodological advances with faster and cheaper hardware as well as…