1 10 20 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/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/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/4f5bcb66e21869e25d79716e409e257a.pdf 01bd4a51e4ab3ef1a04a67af9849a8e9 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 Bayesian Item Response Modeling in R with brms and Stan Subject The topic of the resource : item response theory, Bayesian statistics, R, Stan, brms. Description An account of the resource Item response theory (IRT) is widely applied in the human sciences to model persons’ responses on a set of items measuring one or more latent constructs. While several R packages have been developed that implement IRT models, they tend to be restricted to respective pre-specified classes of models. Further, most implementations are frequentist while the availability of Bayesian methods remains comparably limited. I demonstrate how to use the R package brms together with the probabilistic programming language Stan to specify and fit a wide range of Bayesian IRT models using flexible and intuitive multilevel formula syntax. Further, item and person parameters can be related in both a linear or non-linear manner. Various distributions for categorical, ordinal, and continuous responses are supported. Users may even define their own custom response distribution for use in the presented framework. Common IRT model classes that can be specified natively in the presented framework include 1PL and 2PL logistic models optionally also containing guessing parameters, graded response and partial credit ordinal models, as well as drift diffusion models of response times coupled with binary decisions. Posterior distributions of item and person parameters can be conveniently extracted and postprocessed. Model fit can be evaluated and compared using Bayes factors and efficient cross-validation procedures. Creator An entity primarily responsible for making the resource Paul-Christian Bürkner Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i05 Publisher An entity responsible for making the resource available University of Stuttgart 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/4dbf1445efd7c991d22e1312a9b69caf.pdf ed6ce833088ae0a46595b01993a9224a 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 ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation Subject The topic of the resource ABC, HPC, Spark, MPI, parallel, imbalance, Python library. Description An account of the resource ABCpy is a highly modular scientific library for approximate Bayesian computation (ABC) written in Python. The main contribution of this paper is to document a software engineering effort that enables domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy. We give an overview of the design of ABCpy and provide a performance evaluation concentrating on parallelization. This points us towards the inherent imbalance in some of the ABC algorithms. We develop a dynamic scheduling MPI implementation to mitigate this issue and evaluate the various ABC algorithms according to their adaptability towards high-performance computing Creator An entity primarily responsible for making the resource Ritabrata Dutta Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i07 Publisher An entity responsible for making the resource available University of Warwick 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/1c00125e97f8f6a25456c68eb5b05300.pdf 912c4df1ba2d3a1c389e77183c91044c 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 pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution Subject The topic of the resource : MCMC, Bayesian inference, semiparametric, survival analysis Description An account of the resource In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution. The module is an extension to the open-source program JAGS by which a Gibbs sampler can be applied without requiring the derivation of complete conditionals and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation and is thus more attractive to the researchers aiming to spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo algorithm. For those interested in extending JAGS, this work can be seen as a tutorial including important information not well investigated or organized in other materials. Here, we describe how to use the module taking advantage of the interface between R and JAGS. A short simulation study is developed to ensure that the module behaves well and a real illustration, involving two PE models, exhibits a context where the module can be used in practice. Creator An entity primarily responsible for making the resource Vinícius D. Mayrink Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i08 Publisher An entity responsible for making the resource available Universidade Federal de Minas Gerais 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/4a1ad948776a14998e6842faa01b5ac9.pdf 2870b6557185ccef21cc9796d2fd93e4 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 dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble Subject The topic of the resource : Bayesian inference, diversity patterns, hierarchical models, generalized linear models, Markov chain Monte Carlo, structured residual variance, variance patterns Description An account of the resource Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many biological studies seek to understand non-deterministic patterns in the variance or dispersion of a phenotypic or ecological response variable. We describe a new R package, dalmatian, that provides methods for fitting double hierarchical generalized linear models incorporating fixed and random predictors of both the mean and variance. Models are fit via Markov chain Monte Carlo sampling implemented in either JAGS or nimble and the package provides simple functions for monitoring the sampler and summarizing the results. We illustrate these functions through an application to data on food delivery by breeding pied flycatchers (Ficedula hypoleuca). Our intent is that this package makes it easier for practitioners to implement these models without having to learn the intricacies of Markov chain Monte Carlo methods. Creator An entity primarily responsible for making the resource Simon Bonner Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i10 Publisher An entity responsible for making the resource available University of Western Ontario 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/fe0ec20d6a8aee4649737f08ee5cf9cb.pdf 26cbbb562f59fbc7db21ac3997ade6fd 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 BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression Subject The topic of the resource : seemingly unrelated regression, Bayesian multivariate regression, structured covariance matrix, Markov random field prior, multi-omics data. Description An account of the resource In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with high-dimensional genomic and other omics data, a problem that can be studied with high-dimensional multi-response regression, where the response variables are potentially highly correlated. To this purpose, we recently introduced several multivariate Bayesian variable and covariance selection models, e.g., Bayesian estimation methods for sparse seemingly unrelated regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection prior for latent variable inclusion indicators, which results in sparse variable selection for associations between predictors and multiple phenotypes. We also propose an alternative, which uses a Markov random field (MRF) prior for incorporating prior knowledge about the dependence structure of the inclusion indicators. Inference of Bayesian seemingly unrelated regression (SUR) by Markov chain Monte Carlo methods is made computationally feasible by factorization of the covariance matrix amongst the response variables. In this paper we present BayesSUR, an R package, which allows the user to easily specify and run a range of different Bayesian SUR models, which have been implemented in C++ for computational efficiency. The R package allows the specification of the models in a modular way, where the user chooses the priors for variable selection and for covariance selection separately. We demonstrate the performance of sparse SUR models with the hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for typical applications. Creator An entity primarily responsible for making the resource Zhi Zhao Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v100i11 Publisher An entity responsible for making the resource available University of Oslo 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