https://www.jstatsoft.org/index.php/jss/issue/feed Journal of Statistical Software 2022-09-29T15:55:54+00:00 Editorial Office editor@jstatsoft.org Open Journal Systems The Journal of Statistical Software publishes articles on statistical software along with the source code of the software itself and replication code for all empirical results. https://www.jstatsoft.org/index.php/jss/article/view/v104i01 Statistical Network Analysis with Bergm 2021-06-11T00:24:19+00:00 Alberto Caimo acaimo.stats@gmail.com Lampros Bouranis no@e-mail.provided Robert Krause no@e-mail.provided Nial Friel no@e-mail.provided <p>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 complex dependence structure of network data in a wide range of applied contexts. The Bergm package for R has become a popular package to carry out Bayesian parameter inference, missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs. Over the last few years, the package has been considerably improved in terms of efficiency by adopting some of the state-of-the-art Bayesian computational methods for doublyintractable distributions. Recently, version 5 of the package has been made available on CRAN having undergone a substantial makeover, which has made it more accessible and easy to use for practitioners. New functions include data augmentation procedures based on the approximate exchange algorithm for dealing with missing data, adjusted pseudolikelihood and pseudo-posterior procedures, which allow for fast approximate inference of the ERGM parameter posterior and model evidence for networks on several thousands nodes.</p> 2022-09-29T00:00:00+00:00 Copyright (c) 2022 Alberto Caimo, Lampros Bouranis, Robert Krause, Nial Friel https://www.jstatsoft.org/index.php/jss/article/view/v104i02 ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models 2021-11-01T12:50:38+00:00 Riccardo (Jack) Lucchetti r.lucchetti@univpm.it Luca Pedini l.pedini@staff.univpm.it <p>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 implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatization of the model building procedure and the parallelization of the sampling scheme.</p> 2022-09-29T00:00:00+00:00 Copyright (c) 2022 Riccardo (Jack) Lucchetti, Luca Pedini https://www.jstatsoft.org/index.php/jss/article/view/v104i03 AMR: An R Package for Working with Antimicrobial Resistance Data 2021-01-14T18:29:35+00:00 Matthijs S. Berends m.berends@certe.nl Christian F. Luz c.f.luz@umcg.nl Alexander W. Friedrich alex.friedrich@umcg.nl Bhanu N. M. Sinha b.sinha@umcg.nl Casper J. Albers c.j.albers@rug.nl Corinna Glasner c.glasner@umcg.nl <p>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 guidelines are in place for this process as well as for reporting data on (inter-)national levels. However, there is a gap in the availability of standardized and reproducible tools for working with laboratory data to produce the required reports. It is known that extensive efforts in data cleaning and validation are required when working with data from laboratory information systems. Furthermore, the global spread and relevance of antimicrobial resistance demands to incorporate international reference data in the analysis process. In this paper, we introduce the AMR package for R that aims at closing this gap by providing tools to simplify antimicrobial resistance data cleaning and analysis, while incorporating international guidelines and scientifically reliable reference data. The AMR package enables standardized and reproducible antimicrobial resistance analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. The AMR package works independently of any laboratory information system and provides several functions to integrate into international workflows (e.g., WHONET software provided by the World Health Organization).</p> 2022-09-29T00:00:00+00:00 Copyright (c) 2022 Matthijs S. Berends, Christian F. Luz, Alexander W. Friedrich, Bhanu N. M. Sinha, Casper J. Albers, Corinna Glasner https://www.jstatsoft.org/index.php/jss/article/view/v104i04 Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia 2021-09-01T02:52:21+00:00 Justin Angevaare jangevaa@uoguelph.ca Zeny Feng zfeng@uoguelph.ca Rob Deardon robert.deardon@ucalgary.ca <p>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 parameters within a Bayesian framework via Markov chain Monte Carlo (MCMC). We detail our specific strategies for conducting MCMC for TN-ILMs, and our implementation of these strategies in the Julia package, Pathogen.jl, which leverages key features of the Julia language. We provide an example using Pathogen.jl to simulate an epidemic following a susceptible-infectious-removed (SIR) TN-ILM, and then perform inference using observations that were generated from that epidemic. We also demonstrate the functionality of Pathogen.jl with an application of TN-ILMs to data from a measles outbreak that occurred in Hagelloch, Germany, in 1861 (Pfeilsticker 1863; Oesterle 1992).</p> 2022-09-29T00:00:00+00:00 Copyright (c) 2022 Justin Angevaare, Zeny Feng, Rob Deardon https://www.jstatsoft.org/index.php/jss/article/view/v104i05 calculus: High-Dimensional Numerical and Symbolic Calculus in R 2021-12-21T16:03:22+00:00 Emanuele Guidotti emanuele.guidotti@unine.ch <p>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 Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators and numerical integration in arbitrary orthogonal coordinate systems. The library applies numerical methods when working with functions, or symbolic programming when working with characters or expressions. The package handles multivariate numerical calculus in arbitrary dimensions and coordinates. It implements the symbolic counterpart of the numerical methods whenever possible, without depending on external computer algebra systems. Except for Rcpp, the package has no strict dependencies in order to provide a stable self-contained toolbox that invites re-use.</p> 2022-09-29T00:00:00+00:00 Copyright (c) 2022 Emanuele Guidotti https://www.jstatsoft.org/index.php/jss/article/view/v104i06 Fast Penalized Regression and Cross Validation for Tall Data with the oem Package 2017-04-08T22:41:00+00:00 Jared D. Huling huling@umn.edu Peter Chien peter.chien@wisc.edu <p>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 number of observations is much larger. The orthogonalizing expectation maximization (OEM) algorithm is one approach for computation of penalized models which excels in the big tall data regime. The oem package is an efficient implementation of the OEM algorithm which provides a multitude of computation routines with a focus on big tall data, such as a function for out-of-memory computation, for large-scale parallel computation of penalized regression models. Furthermore, in this paper we propose a specialized implementation of the OEM algorithm for cross validation, dramatically reducing the computing time for cross validation over a naive implementation.</p> 2022-10-24T00:00:00+00:00 Copyright (c) 2022 Jared D. Huling, Peter Chien https://www.jstatsoft.org/index.php/jss/article/view/v104i07 synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data 2021-09-07T01:47:10+00:00 Alex Whitworth whitworth.alex@gmail.com <p>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 annealing. To our knowledge, it is the first R package to provide broadly applicable tools for SMSM with ACS data as well as the first SMSM implementation that uses unequal probability sampling in the simulated annealing algorithm. In this paper, we contextualize these developments within the SMSM literature, provide a hands-on user-guide to package synthACS, present a case study of SMSM related to population dynamics, and note areas for future research.</p> 2022-10-26T00:00:00+00:00 Copyright (c) 2022 Alex Whitworth https://www.jstatsoft.org/index.php/jss/article/view/v104i08 Analyzing Intraday Financial Data in R: The highfrequency Package 2022-05-25T01:58:28+00:00 Kris Boudt kris.boudt@ugent.be Onno Kleen kleen@ese.eur.nl Emil Sjørup emilsjoerup@live.dk <p>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 provides a tool chain required to work with such datasets and to conduct statistical analyses dedicated to spot volatility, jumps, realized measures, and many more. We showcase our implemented routines and models on raw high-frequency data from large stock exchanges.</p> 2022-10-27T00:00:00+00:00 Copyright (c) 2022 Kris Boudt, Onno Kleen, Emil Sjørup https://www.jstatsoft.org/index.php/jss/article/view/v104i09 BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R 2021-11-18T00:50:22+00:00 Maximilian Boeck maximilian.boeck@wu.ac.at Martin Feldkircher martin.feldkircher@da-vienna.ac.at Florian Huber florian.huber@sbg.ac.at <p>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 related to overfitting. This often improves inference as well as out-of-sample forecasts. Computational efficiency is achieved by using C++ to considerably speed up time-consuming functions. To maximize usability, the package includes numerous functions for carrying out structural inference and forecasting. These include generalized and structural impulse response functions, forecast error variance, and historical decompositions as well as conditional forecasts.</p> 2022-10-26T00:00:00+00:00 Copyright (c) 2022 Maximilian Boeck, Martin Feldkircher, Florian Huber https://www.jstatsoft.org/index.php/jss/article/view/v104i10 A Practitioner's Guide and MATLAB Toolbox for Mixed Frequency State Space Models 2020-07-19T21:44:19+00:00 Scott A. Brave sbrave@morningconsult.com R. Andrew Butters rabutter@indiana.edu David Kelley david.kelley@ny.frb.org <p>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 facilitate both estimation and inference in these settings. After presenting a unified treatment of the state space approach to mixed frequency data modeling, we provide a series of applications to demonstrate how our MATLAB toolbox can make the estimation and post-processing of these models straightforward.</p> 2022-10-31T00:00:00+00:00 Copyright (c) 2022 Scott A. Brave, R. Andrew Butters, David Kelley https://www.jstatsoft.org/index.php/jss/article/view/v104i11 spsur: An R Package for Dealing with Spatial Seemingly Unrelated Regression Models 2021-09-09T15:05:10+00:00 Román Mínguez roman.minguez@uclm.es Fernando A. López fernando.lopez@upct.es Jesús Mur jmur@unizar.es <p>Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational econometric specification to simultaneously incorporate spatial effects and correlated error terms across equations. The purpose of the spsur R package is to supply a complete set of functions to test for spatial structures in the residual of a SUR model; to estimate the most popular specifications by applying different methods and test for linear restrictions on the parameters. The package also facilitates the estimation of socalled spatial impacts, conveniently adapted to a SUR framework. The package includes functions to simulate datasets with the features decided by the user, which may be useful in teaching activities or in more general research projects. The article concludes with a real data application showing the potential that spsur has to examine the relation of individual mobility over geographic areas and the incidence of COVID-19 in Spain during the first lockdown.</p> 2022-10-30T00:00:00+00:00 Copyright (c) 2022 Román Mínguez, Fernando A. López, Jesús Mur