1 10 8 https://repository.horizon.ac.id/files/original/ad85bf5b19629aee64f68effa196d74e.pdf 6ef0413feb12973113dd29f0da2b79dd 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 110 Tahun 2024 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 Weighted scoringRules: Emphasizing Particular Outcomes When Evaluating Probabilistic Forecasts Subject The topic of the resource : forecast evaluation, probabilistic forecasting, proper scoring rules, R Description An account of the resource When predicting future events, it is common to issue forecasts that are probabilistic, in the form of probability distributions over the range of possible outcomes. Such forecasts can be evaluated using proper scoring rules. Proper scoring rules condense forecast performance into a single numerical value, allowing competing forecasters to be ranked and compared. To facilitate the use of scoring rules in practical applications, the scoringRules package in R provides popular scoring rules for a wide range of forecast distributions. This paper discusses an extension to the scoringRules package that additionally permits the implementation of popular weighted scoring rules. Weighted scoring rules allow particular outcomes to be targeted during forecast evaluation, recognizing that certain outcomes are often of more interest than others when assessing forecast quality. This introduces the potential for very flexible, user-oriented evaluation of probabilistic forecasts. We discuss the theory underlying weighted scoring rules, and describe how they can readily be implemented in practice using scoringRules. Functionality is available for weighted versions of several popular scoring rules, including the logarithmic score, the continuous ranked probability score, and the energy score. Two case studies are presented to demonstrate this, whereby weighted scoring rules are applied to univariate and multivariate probabilistic forecasts in the fields of meteorology and economic Creator An entity primarily responsible for making the resource Sam Allen Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i08 Publisher An entity responsible for making the resource available ETH Zürich Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/4ee0ce4a1417d0498677c1ea5d069180.pdf 63d3bb6d8ed710c41f56545546c2fd78 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 110 Tahun 2024 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 cubble: An R Package for Organizing and Wrangling Multivariate Spatio-Temporal Data Subject The topic of the resource cubble: An R Package for Organizing and Wrangling Multivariate Spatio-Temporal Data Description An account of the resource Multivariate spatio-temporal data refers to multiple measurements taken across space and time. For many analyses, spatial and time components can be separately studied: for example, to explore the temporal trend of one variable for a single spatial location, or to model the spatial distribution of one variable at a given time. However for some studies, it is important to analyze different aspects of the spatio-temporal data simultaneously, for instance, temporal trends of multiple variables across locations. In order to facilitate the study of different portions or combinations of spatio-temporal data, we introduce a new class, cubble, with a suite of functions enabling easy slicing and dicing on different spatio-temporal components. The proposed cubble class ensures that all the components of the data are easy to access and manipulate while providing flexibility for data analysis. In addition, the cubble package facilitates visual and numerical explorations of the data while easing data wrangling and modelling. The cubble class and the tools implemented in the package are illustrated with examples from climate data analysis. Creator An entity primarily responsible for making the resource H. Sherry Zhang Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i07 Publisher An entity responsible for making the resource available Monash University University of Texas at Austin Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/1ffa91d3b39a85122daebb4d992f7d5e.pdf 08ed4d23cba4cdb396dfa429378c6ac4 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 110 Tahun 2024 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 sparsegl: An R Package for Estimating Sparse Group Lasso Subject The topic of the resource : generalized linear model, regularization, sequential strong rule, sparse matrix Description An account of the resource The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices. Creator An entity primarily responsible for making the resource Xiaoxuan Liang Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i06 Publisher An entity responsible for making the resource available University of British Columbia Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/256abd93cbffcfa0097cc5dabcea4889.pdf 15899f9c9d04caa9be81c609266b0ace 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 110 Tahun 2024 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 bayesnec: An R Package for Concentration-Response Modeling and Estimation of Toxicity Metrics Subject The topic of the resource : concentration-response, no-effect-concentration, Bayesian, non-linear modeling. Description An account of the resource The bayesnec package has been developed for R to fit concentration (dose)-response curves (CR) to toxicity data for the purpose of deriving no-effect-concentration (NEC), nosignificant-effect-concentration (NSEC), and effect-concentration (of specified percentage “x”, ECx) thresholds from non-linear models fitted using Bayesian Hamiltonian Monte Carlo (HMC) via R packages brms and rstan or cmdstanr. In bayesnec it is possible to fit a single model, custom model-set, specific model-set or all of the available models. When multiple models are specified, the bnec() function returns a model weighted average estimate of predicted posterior values. A range of support functions and methods is also included to work with the returned single, or multi-model objects that allow extraction of raw, or model averaged predicted, NEC, NSEC and ECx values and to interrogate the fitted model or model-set. By combining Bayesian methods with model averaging, bayesnec provides a single estimate of toxicity and associated uncertainty that can be directly integrated into risk assessment frameworks. Creator An entity primarily responsible for making the resource Rebecca Fisher Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i05 Publisher An entity responsible for making the resource available Australian Institute of Marine Science Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/5eafc0179ac8b23b58862074649e8ca6.pdf 73a9f22b47f5d6abf9ffd025e3159ae5 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 110 Tahun 2024 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 fairadapt: Causal Reasoning for Fair Data Preprocessing Subject The topic of the resource algorithmic fairness, causal inference, machine learning. Description An account of the resource Machine learning algorithms are useful for various prediction tasks, but they can also learn how to discriminate, based on gender, race or other sensitive attributes. This realization gave rise to the field of fair machine learning, which aims to recognize, quantify and ultimately mitigate such algorithmic bias. This manuscript describes the R package fairadapt, which implements a causal inference preprocessing method. By making use of a causal graphical model alongside the observed data, the method can be used to address hypothetical questions of the form “What would my salary have been, had I been of a different gender/race?”. Such individual level counterfactual reasoning can help eliminate discrimination and help justify fair decisions. We also discuss appropriate relaxations which assume that certain causal pathways from the sensitive attribute to the outcome are not discriminatory. Creator An entity primarily responsible for making the resource Drago Plečko Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i04 Publisher An entity responsible for making the resource available ETH Zürich Date A point or period of time associated with an event in the lifecycle of the resource May 2024 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/d3508c15435997580ab9e5f72afe40c8.pdf 4e383236f9deebde2b13e0c03573f6cf 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 110 Tahun 2024 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 makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R Subject The topic of the resource : Bayesian hierarchical models, robust inference, joint prior distributions, hierarchical variance decomposition, graphical user interface, R. Description An account of the resource Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian hierarchical models. This is particularly important when there are random effects that are hard to identify based on observed data. The challenge lies in understanding and controlling the joint influence of the priors for the variance parameters, and makemyprior is an R package that guides the formulation of joint prior distributions for variances. A joint prior distribution is constructed based on a hierarchical decomposition of the total variance in the model along a tree, and takes the entire model structure into account. Users input their prior beliefs or express ignorance at each level of the tree. Prior beliefs can be general ideas about reasonable ranges of variance values and need not be detailed expert knowledge. The constructed priors lead to robust inference and guarantee proper posteriors. A graphical user interface facilitates construction and assessment of different choices of priors through visualization of the tree and joint prior. The package aims to expand the toolbox of applied researchers and make priors an active component in their Bayesian workflow. Creator An entity primarily responsible for making the resource Ingeborg Hem Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i03 Publisher An entity responsible for making the resource available Norwegian University of Science and Technology Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/cb7c3b74b144cb6f900e62f95c4db38d.pdf 2afead70dcf4f91c9edf6eac42e0c86d 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 110 Tahun 2024 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 An Extendable Python Implementation of Robust Optimization Monte Carlo Subject The topic of the resource Bayesian inference, implicit models, likelihood-free, Python, elf Description An account of the resource Performing inference in statistical models with an intractable likelihood is challenging, therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency limitations. In this paper, we present the implementation of the LFI method robust optimization Monte Carlo (ROMC) in the Python package elfi. ROMC is a novel and efficient (highly-parallelizable) LFI framework that provides accurate weighted samples from the posterior. Our implementation can be used in two ways. First, a scientist may use it as an out-of-the-box LFI algorithm; we provide an easy-to-use API harmonized with the principles of elfi, enabling effortless comparisons with the rest of the methods included in the package. Additionally, we have carefully split ROMC into isolated components for supporting extensibility. A researcher may experiment with novel method(s) for solving part(s) of ROMC without reimplementing everything from scratch. In both scenarios, the ROMC parts can run in a fully-parallelized manner, exploiting all CPU cores. We also provide helpful functionalities for (i) inspecting the inference process and (ii) evaluating the obtained samples. Finally, we test the robustness of our implementation on some typical LFI examples. Creator An entity primarily responsible for making the resource Vasilis Gkolemis Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i02 Publisher An entity responsible for making the resource available ATHENA RC Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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/4201cd0c00366d10a0ccc09c9f020cf2.pdf b97974ff62f1b794d1b6794e6880d183 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 110 Tahun 2024 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 anomaly: Detection of Anomalous Structure in Time Series Data Subject The topic of the resource : anomaly detection, point anomaly, collective anomaly, BARD, CAPA, PASS. Description An account of the resource One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed collective and point anomaly family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package. Creator An entity primarily responsible for making the resource Alex Fisch Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v110i01 Publisher An entity responsible for making the resource available Lancaster University Date A point or period of time associated with an event in the lifecycle of the resource August 2024 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