Volume 104 Tahun 2022
Dublin Core
Title
Volume 104 Tahun 2022
Source
https://www.jstatsoft.org/issue/view/v104
Date
2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Collection Items
Statistical Network Analysis with Bergm
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…
data increasingly amenable to statistical analysis. Exponential random graph models
(ERGMs) emerged as one of the main families of models capable of capturing the…
ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models
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…
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…
AMR: An R Package for Working with Antimicrobial Resistance Data
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…
trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and…
athogen.jl: Infectious Disease Transmission Network Modeling with Julia
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…
can be used to jointly infer transmission networks, event times, and model…
calculus: High-Dimensional Numerical and Symbolic Calculus in R
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…
Fast Penalized Regression and Cross Validation for Tall Data with the oem Package
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…
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…
synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data
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…
and enables to conduct spatial microsimulation modeling (SMSM) via simulated…
Analyzing Intraday Financial Data in R: The highfrequency Package
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…
for pre-processing financial high-frequency data, analyzing intraday stock returns, and
forecasting stock market volatility. For academics and practitioners alike, it…
BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R
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…
autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian
treatment of GVARs allows to include large information sets by mitigating issues…
A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models
The use of mixed frequency data is now common in many applications, ranging from
the analysis of high frequency financial time series to large cross-sections of macroeconomic
time series. In this article, we show how state space methods can easily…
the analysis of high frequency financial time series to large cross-sections of macroeconomic
time series. In this article, we show how state space methods can easily…