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/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 https://repository.horizon.ac.id/files/original/3e6e0ccbc22f07d4ca0f58c99d354508.pdf 421392225a095dd2a1c29b3095d2ccc4 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 Efficient Bayesian Structural Equation Modeling in Stan Subject The topic of the resource Bayesian SEM, blavaan, JAGS, MCMC, structural equation model, Stan. Description An account of the resource 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 interdependent response variables and multiple latent variables (which may also be called random effects or hidden variables), often leading to slow and inefficient posterior sampling. In this paper, we describe and illustrate a general, efficient approach to Bayesian SEM estimation in Stan, contrasting it with previous implementations in R package blavaan (Merkle and Rosseel 2018). After describing the approaches in detail, we conduct a practical comparison under multiple scenarios. The comparisons show that the new approach is clearly better. We also discuss ways that the approach may be extended to other models that are of interest to psychometricians. Creator An entity primarily responsible for making the resource Edgar C. Merkle Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i06 Publisher An entity responsible for making the resource available University of Missouri 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 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/c67fcb9701e692e8495c5e6f6611773c.pdf 1b70f6cd67a8a557d7d7fb3fbf6a06ea 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 New Frontiers in Bayesian Modeling Using the INLA Package in R Subject The topic of the resource INLA, joint model, non-separable, spatial, temporal, R Description An account of the resource 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 results for Bayesian inference, using sparse matrices. The swift uptake of this framework for Bayesian modeling is rooted in the computational efficiency of the approach and catalyzed by the demand presented by the big data era. In this paper, we present new developments within the INLA package with the aim to provide a computationally efficient mechanism for the Bayesian inference of relevant challenging situations Creator An entity primarily responsible for making the resource Janet van Niekerk Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i02 Publisher An entity responsible for making the resource available King Abdullah University of Science and Technology 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/5eb78bc58c77dbac8f3a0df1920ff1c2.pdf 4076b18e3fb26f8d880c15ee0dcd29dd 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 qgam: Bayesian Nonparametric Quantile Regression Modeling in R Subject The topic of the resource : Bayesian quantile regression, generalized additive models, regression splines, calibrated Bayes, fast Bayesian inference, R. Description An account of the resource 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 that the response distribution is modeled parametrically, here we discuss more flexible methods that do not entail any parametric assumption. In particular, this article introduces the qgam package, which is an extension of mgcv providing fast calibrated Bayesian methods for fitting quantile GAMs (QGAMs) in R. QGAMs are based on a smooth version of the pinball loss of Koenker (2005), rather than on a likelihood function, hence jointly achieving satisfactory accuracy of the quantile point estimates and coverage of the corresponding credible intervals requires adopting the specialized Bayesian fitting framework of Fasiolo, Wood, Zaffran, Nedellec, and Goude (2021b). Here we detail how this framework is implemented in qgam and we provide examples illustrating how the package should be used in practi Creator An entity primarily responsible for making the resource Matteo Fasiolo Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i09 Publisher An entity responsible for making the resource available University of Bristo 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/48f0103b1912a205f422dbcfaa6dcc31.pdf d847b2f66bd8d1ba8f490a26d4b5a86f 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 Software for Bayesian Statistics Subject The topic of the resource Bayesian statistics, special issue, R, JAGS, INLA, Stan, nimble. Description An account of the resource 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 model fitting, survival models, clinical trials, missing values, time series, hypothesis testing, priors, approximate Bayesian computation, and others. Creator An entity primarily responsible for making the resource Michela Cameletti Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i01 Publisher An entity responsible for making the resource available Università degli Studi di Bergamo 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/ae59181ec701a0f11da637f019e9969a.pdf d2f90e09c079fa3219bfd407db44b13e 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 Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages Subject The topic of the resource particle filtering, sequential Monte Carlo, auxiliary particle filter, IF2 iterated filtering, ensemble Kalman filter, particle MCMC, R, nimbleSMC, nimble. Description An account of the resource 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, the package allows users to code models in the BUGS language, and it allows users to write algorithms that can be applied to any appropriate model. In this paper, we introduce the nimbleSMC R package. nimbleSMC contains algorithms for state-space model analysis using sequential Monte Carlo (SMC) techniques that are built using nimble. We first provide an overview of state-space models and commonly-used SMC algorithms. We then describe how to build a state-space model in nimble and conduct inference using existing SMC algorithms within nimbleSMC. SMC algorithms within nimbleSMC currently include the bootstrap filter, auxiliary particle filter, ensemble Kalman filter, IF2 method of iterated filtering, and a particle Markov chain Monte Carlo (MCMC) sampler. These algorithms can be run in R or compiled into C++ for more efficient execution. Examples of applying SMC algorithms to linear autoregressive models and a stochastic volatility model are provided. Finally, we give an overview of how model-generic algorithms are coded within nimble by providing code for a simple SMC algorithm. This illustrates how users can easily extend nimble’s SMC methods in high-level code Creator An entity primarily responsible for making the resource Nicholas Michaud Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i03 Publisher An entity responsible for making the resource available University of California, Berkeley 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/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