1 10 9 https://repository.horizon.ac.id/files/original/5360bc1f2b3e416c77e40ecd5b6babb6.pdf 5bdb8e316d9b8e39f0000778460137a7 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 VOL. 112 (2025) 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 BayesMix: Bayesian Mixture Models in C++ Description An account of the resource We describe BayesMix, a C++ library for MCMC posterior simulation for general Bayesian mixture models. The goal of BayesMix is to provide a self-contained ecosystem to perform inference for mixture models to computer scientists, statisticians and practitioners. The key idea of this library is extensibility, as we wish the users to easily adapt our software to their specific Bayesian mixture models. In addition to the several models and MCMC algorithms for posterior inference included in the library, new users with little familiarity on mixture models and the related MCMC algorithms can extend our library with minimal coding effort. Our library is computationally very efficient when compared to competitor software. Examples show that the typical code runtimes are from two to 25 times faster than competitors for data dimension from one to ten. We also provide Python (bayesmixpy) and R (bayesmixr) interfaces. Our library is publicly available on GitHub at https://github.com/bayesmix-dev/bayesmix/. Creator An entity primarily responsible for making the resource Mario Beraha, Bruno Guindani, Matteo Gianella, Alessandra Guglielmi Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i09 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/8ccb336feeaaffb42ef9af29ad33effd.pdf a441d3b7b81815851b4723480c7682b9 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 VOL. 112 (2025) 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 pyrichlet: A Python Package for Density Estimation and Clustering Using Gaussian Mixture Models Subject The topic of the resource Bayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called pyrichlet, for Bayesian nonparametric density estimation and clustering using various state-of-the-art Gaussian mixture models that generalize the well established Dirichlet process mixture, many of which are fairly new. Implementation is performed using Markov chain Monte Carlo techniques as well as variational Bayes methods. This article contains a detailed description of pyrichlet and examples for its usage with a real dataset. Description An account of the resource Bayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called pyrichlet, for Bayesian nonparametric density estimation and clustering using various state-of-the-art Gaussian mixture models that generalize the well established Dirichlet process mixture, many of which are fairly new. Implementation is performed using Markov chain Monte Carlo techniques as well as variational Bayes methods. This article contains a detailed description of pyrichlet and examples for its usage with a real dataset. Creator An entity primarily responsible for making the resource Fidel Selva, Ruth Fuentes-García, María Fernanda Gil-Leyva Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i08 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/b8e38d240047023de2a376674e33e772.pdf 34dfbb8650606051f6da9a0fa405e8ce 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 VOL. 112 (2025) 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 Learning Permutation Symmetry of a Gaussian Vector with gips in R Description An account of the resource The study of hidden structures in data presents challenges in modern statistics and machine learning. We introduce the gips package in R, which identifies permutation subgroup symmetries in Gaussian vectors. gips serves two main purposes: Exploratory analysis in discovering hidden permutation symmetries and estimating the covariance matrix under permutation symmetry. It is competitive to canonical methods in dimensionality reduction while providing a new interpretation of the results. gips implements a novel Bayesian model selection procedure within Gaussian vectors invariant under the permutation subgroup introduced in Graczyk, Ishi, Kołodziejek, and Massam (2022b, The Annals of Statistics). Creator An entity primarily responsible for making the resource Adam Chojecki, Paweł Morgen, Bartosz Kołodziejek Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i07 Publisher An entity responsible for making the resource available OJK/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/0a4d2e61ff2c15fdf8d8cd1b9dfd1af9.pdf 0c44b42a3aff32359cc020e9e9da915a 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 VOL. 112 (2025) 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 TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R Description An account of the resource Knowing how individual abilities change is essential in a wide range of activities. The most widely used skill estimators in industry and academia (such as Elo and TrueSkill) propagate information in only one direction, from the past to the future, preventing them from obtaining reliable initial estimates and ensuring comparability between estimates distant in time and space. In contrast, the model TrueSkill Through Time (TTT) propagates all historical information throughout a single causal network, providing estimates with low uncertainty at any given time, enabling reliable initial skill estimates, and ensuring historical comparability. Although the TTT model was published more than a decade ago, it was not available until now in the programming languages with the largest communities. Here we offer the first software for Julia, Python, and R, accompanied by a detailed overview for the general public and an in-depth scientific explanation. After illustrating its basic mode of use, we show how to estimate the learning curves of historical players of the Association of Tennis Professionals. Analytical approximation methods and message-passing algorithms allow inference to be solved efficiently using any low-end computer, even in causal networks with millions of nodes and irregular structures. Creator An entity primarily responsible for making the resource Gustavo Landfried, Esteban Mocskos Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i06 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 11 APRIL 2025 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/9f3c44d6ab8c73ac6caf25c99798c8f6.pdf 9811178356ca16bbff3e971236603a3a 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 VOL. 112 (2025) 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 Stability Selection and Consensus Clustering in R: The R Package sharp Description An account of the resource The R package sharp (Stability-enHanced Approaches using Resampling Procedures) provides an integrated framework for stability-enhanced variable selection, graphical modeling and clustering. In stability selection, a feature selection algorithm is combined with a resampling technique to estimate feature selection probabilities. Features with selection proportions above a threshold are considered stably selected. Similarly, a clustering algorithm is applied on multiple subsamples of items to compute co-membership proportions in consensus clustering. The consensus clusters are obtained by clustering using comembership proportions as a measure of similarity. We calibrate the hyper-parameters of stability selection (or consensus clustering) jointly by maximizing a consensus score calculated under the null hypothesis of equiprobability of selection (or co-membership), which characterizes instability. The package offers flexibility in the modeling, includes diagnostic and visualization tools, and allows for parallelization. Creator An entity primarily responsible for making the resource Barbara Bodinier, Sabrina Rodrigues, Maryam Karimi, Sarah Filippi, Julien Chiquet, Marc Chadeau-Hyam Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i05 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 12 APRIL 2025 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/37124e55b1b298aa6be825ae88722e7d.pdf fbc201c6fa57ce66c20503f2d3f87544 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 VOL. 112 (2025) 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 Split-Apply-Combine with Dynamic Grouping Description An account of the resource Partitioning a data set by one or more of its attributes and computing an aggregate for each part is one of the most common operations in data analyses. There are use cases where the partitioning is determined dynamically by collapsing smaller subsets into larger ones, to ensure sufficient support for the computed aggregate. These use cases are not supported by software implementing split-apply-combine types of operations. This paper presents the R package accumulate that offers convenient interfaces for defining grouped aggregation where the grouping itself is dynamically determined, based on user-defined conditions on subsets, and a user-defined subset collapsing scheme. The formal underlying algorithm is described and analyzed as well. Creator An entity primarily responsible for making the resource Mark P. J. van der Loo Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i04 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/3c4b08cac112f1103fb8dc3bf4af3f46.pdf 210446d501af0308012ec649ea86918d 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 VOL. 112 (2025) 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 RESI: An R Package for Robust Effect Sizes Description An account of the resource ffect size indices are useful parameters that quantify the strength of association and are unaffected by sample size. There are many available effect size parameters and estimators, but it is difficult to compare effect sizes across studies as most are defined for a specific type of population parameter. We recently introduced a new robust effect size index (RESI) and confidence interval, which is advantageous because it is not model-specific. Here we present the RESI R package, which makes it easy to report the RESI and its confidence interval for many different model classes, with a consistent interpretation across parameters and model types. The package produces coefficient, ANOVA tables, and overall Wald tests for model inputs, appending the RESI estimate and confidence interval to each. The package also includes functions for visualization and conversions to and from other effect size measures. For illustration, we analyze and interpret three datasets using different model types. Creator An entity primarily responsible for making the resource Megan Jones, Kaidi Kang, Simon Vandekar Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i03 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/d501d418af973a42849bf07c40a5e0d9.pdf c32a9d44ef4ddaddf0f18cd7e449f88f 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 VOL. 112 (2025) 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 gptools: Scalable Gaussian Process Inference with Stan Description An account of the resource Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We implement two methods for scaling GP inference in Stan: First, a general sparse approximation using a directed acyclic dependency graph; second, a fast, exact method for regularly spaced data modeled by GPs with stationary kernels using the fast Fourier transform. Based on benchmark experiments, we offer guidance for practitioners to decide between different methods and parameterizations. We consider two real-world examples to illustrate the package. The implementation follows Stan's design and exposes performant inference through a familiar interface. Full posterior inference for ten thousand data points is feasible on a laptop in less than 20 seconds. Details on how to get started using the popular interfaces cmdstanpy for Python and cmdstanr for R are provided. Creator An entity primarily responsible for making the resource Till Hoffmann, Jukka-Pekka Onnela Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i02 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 29 MARET 2025 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/0a8b67165c649c6067b2e13934329e0d.pdf 27acfcae3e1a106558467f8413af9802 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 VOL. 112 (2025) 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 Parsimoniously Fitting Large Multivariate Random Effects in glmmTMB Description An account of the resource Multivariate random effects with unstructured variance-covariance matrices of large dimensions, q, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of d < q latent variables. By adding reduced-rank functionality to the package glmmTMB, we enhance the mixed models available to include random effects of dimensions that were previously not possible. We apply the reduced-rank random effect to two examples, estimating a generalized latent variable model for multivariate abundance data and a random-slopes model. Creator An entity primarily responsible for making the resource Maeve McGillycuddy, Gordana Popovic, Benjamin M. Bolker, David I. Warton Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v112i01 Publisher An entity responsible for making the resource available OJS/PKP Date A point or period of time associated with an event in the lifecycle of the resource 11 APRIL 2025 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