1 10 20 https://repository.horizon.ac.id/files/original/c7bced3f5dba4cdbe8c3806819b80e4d.pdf e90f89b877eb3dda6c116d8f864f6b61 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data Subject The topic of the resource Bayesian statistics, clinical trials, historical controls, power prior, R Description An account of the resource This article introduces the R package BayesCTDesign for two-arm randomized Bayesian trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The package BayesCTDesign, which is available from the Comprehensive R Archive Network, has two simulation functions, historic_sim() and simple_sim() for studying trial characteristics under user-defined scenarios, and two methods print() and plot() for displaying summaries of the simulated trial characteristics. The package BayesCTDesign works with two-arm trials with equal sample sizes per arm. The package BayesCTDesign allows a user to study Gaussian, Poisson, Bernoulli, Weibull, lognormal, and piecewise exponential outcomes. Power for two-sided hypothesis tests at a user-defined α is estimated via simulation using a test within each simulation replication that involves comparing a 95% credible interval for the outcome specific treatment effect measure to the null case value. If the 95% credible interval excludes the null case value, then the null hypothesis is rejected, else the null hypothesis is accepted. In the article, the idea of including historical control data in a Bayesian analysis is reviewed, the estimation process of BayesCTDesign is explained, and the user interface is described. Finally, the BayesCTDesign is illustrated via several examples. Creator An entity primarily responsible for making the resource Barry S. Eggleston Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i21 Publisher An entity responsible for making the resource available RTI International Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/135e598bd389f1b74537e6b57c7b5430.pdf 45892840c126ee1bd5586326313fa444 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource ointAI: Joint Analysis and Imputation of Incomplete Data in R Subject The topic of the resource : imputation, Bayesian, missing covariate, nonlinear, interaction, multi-level, survival, joint model, R, JAG Description An account of the resource Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modeling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In settings involving nonlinear associations or interactions, however, incompatibility of the imputation model with the analysis model is an issue often resulting in bias. Similarly, complex outcomes such as longitudinal or survival outcomes cannot be adequately handled by standard implementations. In this paper, we introduce the R package JointAI, which utilizes the Bayesian framework to perform simultaneous analysis and imputation in regression models with incomplete covariates. Using a fully Bayesian joint modeling approach it overcomes the issue of uncongeniality while retaining the attractive flexibility of fully conditional specification multiple imputation by specifying the joint distribution of analysis and imputation models as a sequence of univariate models that can be adapted to the type of variable. JointAI provides functions for Bayesian inference with generalized linear and generalized linear mixed models and extensions thereof as well as survival models and joint models for longitudinal and survival data, that take arguments analogous to the corresponding well known functions for the analysis of complete data from base R and other packages. Usage and features of JointAI are described and illustrated using various examples and the theoretical background is outlined. Creator An entity primarily responsible for making the resource Nicole S. Erler Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i20 Publisher An entity responsible for making the resource available Erasmus Medical Center Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/c1c478d7ca60e2d7da350774be36b544.pdf 7225fc0a28d3a33200b29f90e640885f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Applying Meta-Analytic-Predictive Priors with the R Bayesian Evidence Synthesis Tools Subject The topic of the resource Bayesian inference, clinical trial, extrapolation, historical control, operating characteristics, prior, probability of success, robust analysis. Description An account of the resource Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical data. In this paper, we introduce the package RBesT (R Bayesian evidence synthesis tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by Markov chain Monte Carlo (MCMC). The MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations. Creator An entity primarily responsible for making the resource Sebastian Weber Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i19 Publisher An entity responsible for making the resource available Novartis Pharma AG Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/bd54af232f9efe834a6d37a9918068ed.pdf b06fd86bf6dadfd01fc0fbb285ad789a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BFpack: Flexible Bayes Factor Testing of Scientific Theories in R Subject The topic of the resource : Bayes factors, hypothesis testing, equality/order constrained hypotheses, R. Description An account of the resource There have been considerable methodological developments of Bayes factors for hypothesis testing in the social and behavioral sciences, and related fields. This development is due to the flexibility of the Bayes factor for testing multiple hypotheses simultaneously, the ability to test complex hypotheses involving equality as well as order constraints on the parameters of interest, and the interpretability of the outcome as the weight of evidence provided by the data in support of competing scientific theories. The available software tools for Bayesian hypothesis testing are still limited however. In this paper we present a new R package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools for (i) Bayesian exploratory testing (e.g., zero vs positive vs negative effects), (ii) Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random. Creator An entity primarily responsible for making the resource Joris Mulder Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i18 Publisher An entity responsible for making the resource available Tilburg University Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/70108d84fc3a6b568e5f1cddf2ac436f.pdf 8ec7c1bedbdf4adc10e9f04b80731b45 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Informed Bayesian Inference for the A/B Test Subject The topic of the resource : model comparison, Bayes factor, prior elicitation, Bayesian estimation. Description An account of the resource Booming in business and a staple analysis in medical trials, the A/B test assesses the effect of an intervention or treatment by comparing its success rate with that of a control condition. Across many practical applications, it is desirable that (1) evidence can be obtained in favor of the null hypothesis that the treatment is ineffective; (2) evidence can be monitored as the data accumulate; (3) expert prior knowledge can be taken into account. Most existing approaches do not fulfill these desiderata. Here we describe a Bayesian A/B procedure based on Kass and Vaidyanathan (1992) that allows one to monitor the evidence for the hypotheses that the treatment has either a positive effect, a negative effect, or, crucially, no effect. Furthermore, this approach enables one to incorporate expert knowledge about the relative prior plausibility of the rival hypotheses and about the expected size of the effect, given that it is non-zero. To facilitate the wider adoption of this Bayesian procedure we developed the abtest package in R. We illustrate the package options and the associated statistical results with a fictitious business example and a real data medical example Creator An entity primarily responsible for making the resource Quentin F. Gronau Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i17 Publisher An entity responsible for making the resource available University of Amsterdam Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/7381c5f547861d9e86a4393771205fa2.pdf 387aa9dd7a7f23902580053e5f1fca0e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R Subject The topic of the resource : Bayesian data analysis, dissimilarity matrices, information criteria, multidimensional scaling, MCMC, MDS, mixture models, model-based clustering, three-way MDS. Description An account of the resource We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we consider the case when S matrices are available, each describing the dissimilarities among the same n objects, possibly expressed by S subjects (judges), or measured under different experimental conditions, or with reference to different characteristics of the objects themselves. In particular, we focus on binary dissimilarities, taking values 0 or 1 depending on whether or not two objects are deemed as dissimilar. We are interested in analyzing such data using multidimensional scaling (MDS). Differently from standard MDS algorithms, our goal is to cluster the dissimilarity matrices and, simultaneously, to extract an MDS configuration specific for each cluster. To this end, we develop a fully Bayesian three-way MDS approach, where the elements of each dissimilarity matrix are modeled as a mixture of Bernoulli random vectors. The parameter estimates and the MDS configurations are derived using a hybrid Metropolis-Gibbs Markov Chain Monte Carlo algorithm. We also propose a BIC-like criterion for jointly selecting the optimal number of clusters and latent space dimensions. We illustrate our approach referring both to synthetic data and to a publicly available data set taken from the literature. For the sake of efficiency, the core computations in the package are implemented in C/C++. The package also allows the simulation of multiple chains through the support of the parallel package. Creator An entity primarily responsible for making the resource Sergio Venturini Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i16 Publisher An entity responsible for making the resource available Università Cattolica del Sacro Cuore Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/a61481009c37284ada75d942b8f65496.pdf c30022692ad9915b646b83b5136153c8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures Subject The topic of the resource : Bayesian nonparametric mixture, C++, multivariate density estimation, clustering, importance conditional sampler, slice sampler, marginal sampler. Description An account of the resource BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models. A variety of model specifications and state-of-the-art posterior samplers are implemented. In order to achieve computational efficiency, all sampling methods are written in C++ and seamless integrated into R by means of the Rcpp and RcppArmadillo packages. BNPmix exploits the ggplot2 capabilities and implements a series of generic functions to plot and print summaries of posterior densities and induced clustering of the data. Creator An entity primarily responsible for making the resource Riccardo Corradin Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i15 Publisher An entity responsible for making the resource available University of Milano-Bicocca Date A point or period of time associated with an event in the lifecycle of the resource November 2020 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/43c0f614c7300e80fc5a47db0c085cf1.pdf bd8b21787b924e8edff3159ab17f0d07 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R Subject The topic of the resource vector autoregression, VAR, multivariate, time series, macroeconomics, structural analysis, hierarchical model, forecast, impulse response, identification, Minnesota prior, FRED-MD, Bayesian econometrics Description An account of the resource Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these priors is subject of much debate and can be approached via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models with hierarchical prior selection. It implements functionalities and options that permit addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. Features include structural analysis of impulse responses, forecasts, the most commonly used conjugate priors, as well as a framework for defining custom dummy-observation priors. BVAR makes Bayesian VAR models user-friendly and provides an accessible reference implementation. Creator An entity primarily responsible for making the resource Nikolas Kuschnig Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i14 Publisher An entity responsible for making the resource available WU Vienna University of Economics and Business Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/2f513f8dca2e946a25c4c909b92cdff0.pdf 52bc7776af6471903eaeb0dcd5d772bb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP Subject The topic of the resource Bayesian inference, Gibbs sampler, Markov chain Monte Carlo (MCMC), normalgamma prior, time-varying parameter (TVP) models, log-predictive density scores. Description An account of the resource Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TVP models is well known. This issue can be dealt with using appropriate global-local shrinkage priors, which pull time-varying parameters towards static ones. In this paper, we introduce the R package shrinkTVP (Knaus, Bitto-Nemling, Cadonna, and FrühwirthSchnatter 2021), which provides a fully Bayesian implementation of shrinkage priors for TVP models, taking advantage of recent developments in the literature, in particular those of Bitto and Frühwirth-Schnatter (2019) and Cadonna, Frühwirth-Schnatter, and Knaus (2020). The package shrinkTVP allows for posterior simulation of the parameters through an efficient Markov Chain Monte Carlo scheme. Moreover, summary and visualization methods, as well as the possibility of assessing predictive performance through log-predictive density scores, are provided. The computationally intensive tasks have been implemented in C++ and interfaced with R. The paper includes a brief overview of the models and shrinkage priors implemented in the package. Furthermore, core functionalities are illustrated, both with simulated and real data. Creator An entity primarily responsible for making the resource Peter Knaus Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i13 Publisher An entity responsible for making the resource available WU Vienna Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/3a015e5e7ca0547917d92383b5f4d93a.pdf 279a7d12bb433ab9218b01cbb61141f3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOLUME 100 Tahun 2021 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v100 Date A point or period of time associated with an event in the lifecycle of the resource 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvol Subject The topic of the resource Bayesian inference, state-space model, heteroskedasticity, dynamic correlation, dynamic covariance, factor stochastic volatility, Markov chain Monte Carlo, MCMC, leverage effect, asymmetric return distribution, heavy tails, financial time series Description An account of the resource Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial and software that allows to easily fit SV models to data is rare. We aim to alleviate this issue by presenting novel implementations of five SV models delivered in two R packages. Several unique features are included and documented. As opposed to previous versions, stochvol is now capable of handling linear mean models, conditionally heavy tails, and the leverage effect in combination with SV. Moreover, we newly introduce factorstochvol which caters for multivariate SV. Both packages offer a user-friendly interface through the conventional R generics and a range of tailor-made methods. Computational efficiency is achieved via interfacing R to C++ and doing the heavy work in the latter. In the paper at hand, we provide a detailed discussion on Bayesian SV estimation and showcase the use of the new software through various examples Creator An entity primarily responsible for making the resource Darjus Hosszejni Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i12 Publisher An entity responsible for making the resource available WU Vienna University of Economics and Busines Date A point or period of time associated with an event in the lifecycle of the resource November 2021 Contributor An entity responsible for making contributions to the resource Fajar bagus W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource Text