1 10 5 https://repository.horizon.ac.id/files/original/a9b237532864fb50f16ae76b727731fc.pdf a884f0a95e801f01234a78e40f5f57b2 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. 114 (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 NUBO: A Transparent Python Package for Bayesian Optimization Description An account of the resource NUBO, short for Newcastle University Bayesian Optimisation, is a Bayesian optimization framework for the optimization of expensive-to-evaluate black-box functions, such as physical experiments and computer simulators. Bayesian optimization is a costefficient optimization strategy that uses surrogate modelling via Gaussian processes to represent an objective function and acquisition functions to guide the selection of candidate points to approximate the global optimum of the objective function. NUBO itself focuses on transparency and user experience to make Bayesian optimization easily accessible to researchers from all disciplines. Clean and understandable code, precise references, and thorough documentation ensure transparency, while user experience is ensured by a modular and flexible design, easy-to-write syntax, and careful selection of Bayesian optimization algorithms. NUBO allows users to tailor Bayesian optimization to their specific problem by writing the optimization loop themselves using the provided building blocks. It supports sequential single-point, parallel multi-point, and asynchronous optimization of bounded, constrained, and/or mixed (discrete and continuous) parameter input spaces. Only algorithms and methods that are extensively tested and validated to perform well are included in NUBO. This ensures that the package remains compact and does not overwhelm the user with an unnecessarily large number of options. The package is written in Python but does not require expert knowledge of Python to optimize your simulators and experiments. NUBO is distributed as open-source software under the BSD 3-Clause license Creator An entity primarily responsible for making the resource Mike Diessner, Kevin J. Wilson, Richard D. Whalley Main Article Content Abstract Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v114i01 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 SEPTEMBER 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/8b6ce789485d56ab449bcc1106ea1a74.pdf 3d2e4aecb867b5227e4a79311f146ae3 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. 114 (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 mdendro: An R Package for Extended Agglomerative Hierarchical Clustering Description An account of the resource mdendro is an R package that provides a comprehensive collection of linkage methods for agglomerative hierarchical clustering on a matrix of proximity data (distances or similarities), returning a multifurcated dendrogram or multidendrogram. Multidendrograms can group more than two clusters at the same time, solving the nonuniqueness problem that arises when there are ties in the data. This problem causes that different binary dendrograms are possible depending both on the order of the input data and on the criterion used to break ties. Weighted and unweighted versions of the most common linkage methods are included in the package, which also implements two parametric linkage methods. In addition, package mdendro provides five descriptive measures to analyze the resulting dendrograms: cophenetic correlation coefficient, space distortion ratio, agglomerative coefficient, chaining coefficient and tree balance. Creator An entity primarily responsible for making the resource Alberto Fernández, Sergio Gómez Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v114i02 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 SEPTEMBER 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/ad9e994c787034c9f8cbbb919fabc9c0.pdf 8982b5f71e04551197e0229eccdb5c28 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. 114 (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 ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families Description An account of the resource The empirical Bayes normal means (EBNM) model is important to many areas of statistics, including (but not limited to) multiple testing, wavelet denoising, and gene expression analysis. There are several existing software packages that can fit EBNM models under different prior assumptions and using different algorithms. However, the differences across interfaces complicate direct comparisons, and a number of important prior assumptions do not yet have implementations. Motivated by these issues, we developed the R package ebnm, which provides a unified interface for efficiently fitting EBNM models using a variety of prior assumptions, including nonparametric approaches. In some cases, we incorporated existing implementations into ebnm; in others, we implemented new fitting procedures, with an emphasis on speed and numerical stability. We illustrate the use of ebnm in a detailed analysis of baseball statistics. By providing a unified and easily extensible interface, ebnm can facilitate development of new methods in statistics, genetics, and other areas; as an example, we briefly discuss the R package flashier, which harnesses ebnm for flexible and robust matrix factorization. Creator An entity primarily responsible for making the resource Jason Willwerscheid, Peter Carbonetto, Matthew Stephens Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v114i03 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 SEPTEMBER 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/07c3187c46eaac137be391f834a5f2f3.pdf 9ea5311503090dd660593be1093801eb 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. 114 (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 Exploring Data Subsets with vtree Description An account of the resource Variable trees are a new method for the exploration of discrete multivariate data. They display nested subsets and corresponding frequencies and percentages. Manual calculation of these quantities can be laborious, especially when there are many multi-level factors and missing data. Here we introduce variable trees and their implementation in the vtree R package, draw comparisons with existing methods (contingency tables, mosaic plots, Venn/Euler diagrams, and UpSet), and illustrate their utility using two case studies. Variable trees can be used to (1) reveal patterns in nested subsets, (2) explore missing data, and (3) generate study flow diagrams (e.g., CONSORT diagrams) directly from data frames, to support reproducible research and open science. Creator An entity primarily responsible for making the resource Nick Barrowman, Richard J. Webster Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v114i04 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 14 SEPTEMBER 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/8c99d4cc3760f0ef3f8c6eeece1d0aea.pdf 9ea5311503090dd660593be1093801eb 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. 114 (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 hmmTMB: Hidden Markov Models with Flexible Covariate Effects in R Description An account of the resource Hidden Markov models (HMMs) are widely applied in studies where a discrete-valued process of interest is observed indirectly. They have for example been used to model behavior from human and animal tracking data, disease status from medical data, and financial market volatility from stock prices. The model has two main sets of parameters: transition probabilities, which drive the latent state process, and observation parameters, which characterize the state-dependent distributions of observed variables. One particularly useful extension of HMMs is the inclusion of covariates on those parameters, to investigate the drivers of state transitions or to implement Markov-switching regression models. We present the new R package hmmTMB for HMM analyses, with flexible covariate models in both the hidden state and observation parameters. In particular, non-linear effects are implemented using penalized splines, including multiple univariate and multivariate splines, with automatic smoothness selection. The package allows for various random effect formulations (including random intercepts and slopes), to capture between-group heterogeneity. hmmTMB can be applied to multivariate observations, and it accommodates various types of response data, including continuous (bounded or not), discrete, and binary variables. Parameter constraints can be used to implement non-standard dependence structures, such as semi-Markov, higher-order Markov, and autoregressive models. Here, we summarize the relevant statistical methodology, we describe the structure of the package, and we present an example analysis of animal tracking data to showcase the workflow of the package. Creator An entity primarily responsible for making the resource Théo Michelot Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v114i05 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 SEPTEMBER 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