1 10 12 https://repository.horizon.ac.id/files/original/c2801a49e027619eeb039e634a9163d8.pdf 6ec765c5bba41f9671d216efc52754e9 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 scikit-fda: A Python Package for Functional Data Analysis Subject The topic of the resource : functional data analysis, computational statistics, interactive data visualization, Python, scikit-lear Description An account of the resource The library scikit-fda is a Python package for functional data analysis (FDA). It provides a comprehensive set of tools for representation, preprocessing, and exploratory analysis of functional data. The library is built upon and integrated in Python’s scientific ecosystem. In particular, it conforms to the scikit-learn application programming interface so as to take advantage of the functionality for machine learning provided by this package: Pipelines, model selection, and hyperparameter tuning, among others. The scikit-fda package has been released as free and open-source software under a 3-clause BSD license and is open to contributions from the FDA community. The library’s extensive documentation includes step-by-step tutorials and detailed examples of use Creator An entity primarily responsible for making the resource Carlos Ramos-Carreño Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v109i02 Publisher An entity responsible for making the resource available Universidad Autónoma de Madrid Date A point or period of time associated with an event in the lifecycle of the resource May 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/7e2746dff124771f5ea7f4b8e9366c36.pdf bbfbf87be288f8fa40ec1d02841a603d 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 bizicount: Bivariate Zero-Inflated Count Copula Regression Using R Subject The topic of the resource zero-inflated, bivariate, copula, Poisson, negative binomial, regression, R Description An account of the resource Two common issues arise in regression modelling of bivariate count data: (i) dependence across outcomes, and (ii) excess zero counts (i.e., zero inflation). However, there are currently few options to estimate bivariate zero-inflated count regression models in R. Therefore, we present an R package, bizicount, that enables researchers to easily estimate bivariate zero-inflated count copula regression models. By using copulas to model the dependence across outcomes, researchers do not have to make assumptions about the multivariate (and zero-inflated) structure relating their count variables to one another. Instead, they are only required to make familiar assumptions about the marginal distribution of each outcome variable, which should enable wider use of our approach. Below we present our proposed estimator, detail its advantages over existing alternatives, and demonstrate the use of the corresponding functions for bivariate modeling of terrorism data from Nigeria. Creator An entity primarily responsible for making the resource John M. Niehaus Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v109i01 Publisher An entity responsible for making the resource available Texas A&M University 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/8bd4eb5369cc0e7b709b436d555fd292.pdf 191b28fe4336fa4695a0df289dca2a9a 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal Data Using FRK Subject The topic of the resource areal data, basis functions, big data, change-of-support, fixed rank kriging, nonGaussian data, spatial statistics Description An account of the resource Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. In this paper, we describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a generalized linear mixed model framework. These vastly more general spatial and spatiotemporal models are fitted using the Laplace approximation via the software TMB. The existing functionality of FRK is retained with this advance into non-Gaussian models; in particular, it allows for automatic basis-function construction, it can handle both pointreferenced and areal data simultaneously, and it can predict process values at any spatial support from these data. This new version of FRK also allows for the use of a large number of basis functions when modeling the spatial process, and thus it is often able to achieve more accurate predictions than previous versions of the package in a Gaussian setting. We demonstrate innovative features in this new version of FRK, highlight its ease of use, and compare it to alternative packages using both simulated and real data set Creator An entity primarily responsible for making the resource Matthew Sainsbury-Dale Source A related resource from which the described resource is derived file:///C:/Users/User/Downloads/v108i10%20(2).pdf Publisher An entity responsible for making the resource available atthew Sainsbury-Dale University of Wollongong Date A point or period of time associated with an event in the lifecycle of the resource March 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/ab1e755be7dd9633e4deb7881c0c12c4.pdf 8050ab041922ec3750691b8f7758682e 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 Nonstationary Financial Volatility with the R Package tvgarch Subject The topic of the resource tvgarch, financial volatility, nonstationary GARCH models, time-varying parameter models Description An account of the resource Certain events can make the structure of volatility of financial returns to change, making it nonstationary. Models of time-varying conditional variance such as generalized autoregressive conditional heteroscedasticity (GARCH) models usually assume stationarity. However, this assumption can be inappropriate and volatility predictions can fail in the presence of structural changes in the unconditional variance. To overcome this problem, in the time-varying (TV-)GARCH model, the GARCH parameters are allowed to vary smoothly over time by assuming not only the conditional but also the unconditional variance to be time-varying. In this paper, we show how useful the R package tvgarch (Campos-Martins and Sucarrat 2023) can be for modeling nonstationary volatility in financial empirical applications. The functions for simulating, testing and estimating TV-GARCH-X models, where additional covariates can be included, are implemented in both univariate and multivariate settings. Creator An entity primarily responsible for making the resource Susana Campos-Martins Source A related resource from which the described resource is derived file:///C:/Users/User/Downloads/v108i09.pdf Publisher An entity responsible for making the resource available Catholic University of Portugal University of Oxford Date A point or period of time associated with an event in the lifecycle of the resource March 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/31e12af86ae1c07d2024c54b3c04872d.pdf 2a98af995bde8c593fbe98bd2491f0d8 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 salmon: A Symbolic Linear Regression Package for Python Subject The topic of the resource linear regression, linear model, visualization, model building, Python Description An account of the resource One of the most attractive features of R is its linear modeling capabilities. We describe a Python package, salmon, that brings the best of R’s linear modeling functionality to Python in a Pythonic way – by providing composable objects for specifying and fitting linear models. This object-oriented design also enables other features that enhance easeof-use, such as automatic visualizations and intelligent model building. Creator An entity primarily responsible for making the resource Alex Boyd Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v108i08 Publisher An entity responsible for making the resource available University of California, Irvine Date A point or period of time associated with an event in the lifecycle of the resource March 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/f3fd0a96e0b0bc65e6ec6e3a5708ca04.pdf 4d632dbe9d4e600eb67e87d3e62b0169 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 Holistic Generalized Linear Models Subject The topic of the resource algorithmic regression, best subset selection, conic programming, holistic constraints, optimization, R. Description An account of the resource Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The R package holiglm provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art mixed-integer conic solvers, the package can reliably solve generalized linear models for Gaussian, binomial and Poisson responses with a multitude of holistic constraints. The high-level interface simplifies the constraint specification and can be used as a drop-in replacement for the stats::glm() function. Creator An entity primarily responsible for making the resource Benjamin Schwendinger Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v108i07 Publisher An entity responsible for making the resource available Technische Universität Wien Date A point or period of time associated with an event in the lifecycle of the resource February 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/270fa3963cb96a37d125da9762aa9cb2.pdf a4ae0890678b8851d643b0a0b94e10f3 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 PUMP: Estimating Power, Minimum Detectable Effect Size, and Sample Size When Adjusting for Multiple Outcomes in Multi-Level Experiments Subject The topic of the resource power, multiple testing, multi-level models, randomized controlled trials. Description An account of the resource For randomized controlled trials (RCTs) with a single intervention’s impact being measured on multiple outcomes, researchers often apply a multiple testing procedure (such as Bonferroni or Benjamini-Hochberg) to adjust p values. Such an adjustment reduces the likelihood of spurious findings, but also changes the statistical power, sometimes substantially. A reduction in power means a reduction in the probability of detecting effects when they do exist. This consideration is frequently ignored in typical power analyses, as existing tools do not easily accommodate the use of multiple testing procedures. We introduce the PUMP (Power Under Multiplicity Project) R package as a tool for analysts to estimate statistical power, minimum detectable effect size, and sample size requirements for multi-level RCTs with multiple outcomes. PUMP uses a simulation-based approach to flexibly estimate power for a wide variety of experimental designs, number of outcomes, multiple testing procedures, and other user choices. By assuming linear mixed effects models, we can draw directly from the joint distribution of test statistics across outcomes and thus estimate power via simulation. One of PUMP’s main innovations is accommodating multiple outcomes, which are accounted for in two ways. First, power estimates from PUMP properly account for the adjustment in p values from applying a multiple testing procedure. Second, when considering multiple outcomes rather than a single outcome, different definitions of statistical power emerge. PUMP allows researchers to consider a variety of definitions of power in order to choose the most appropriate types of power for the goals of their study. The package supports a variety of commonly used frequentist multi-level RCT designs and linear mixed effects models. In addition to the main functionality of estimating power, minimum detectable effect size, and sample size requirements, the package allows the user to easily explore sensitivity of these quantities to changes in underlying assumptions Creator An entity primarily responsible for making the resource Kristen B. Hunter Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v108i06 Publisher An entity responsible for making the resource available risten B. Hunter University of New South Wales Date A point or period of time associated with an event in the lifecycle of the resource February 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/d055181093b6f9ec3051e9acc020c879.pdf e0bcabd018622c620aaea078bef4612f 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 melt: Multiple Empirical Likelihood Tests in R Subject The topic of the resource empirical likelihood, generalized linear models, hypothesis testing, optimization, R Description An account of the resource Empirical likelihood enables a nonparametric, likelihood-driven style of inference without relying on assumptions frequently made in parametric models. Empirical likelihoodbased tests are asymptotically pivotal and thus avoid explicit studentization. This paper presents the R package melt that provides a unified framework for data analysis with empirical likelihood methods. A collection of functions are available to perform multiple empirical likelihood tests for linear and generalized linear models in R. The package melt offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. Hypothesis testing uses a projected gradient algorithm to solve constrained empirical likelihood optimization problems. The core computational routines are implemented in C++, with OpenMP for parallel computation. Creator An entity primarily responsible for making the resource Eunseop Kim Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v108i05 Publisher An entity responsible for making the resource available The Ohio State University Date A point or period of time associated with an event in the lifecycle of the resource February 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/6cb757541044ca8d43338ebb4934f23f.pdf 08a38f1b6079151354ae1f50bdaba6eb 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 gcimpute: A Package for Missing Data Imputation Subject The topic of the resource missing data, single imputation, multiple imputation, Gaussian copula, mixed data, imputation uncertainty, Python. Description An account of the resource This article introduces the Python package gcimpute for missing data imputation. Package gcimpute can impute missing data with many different variable types, including continuous, binary, ordinal, count, and truncated values, by modeling data as samples from a Gaussian copula model. This semiparametric model learns the marginal distribution of each variable to match the empirical distribution, yet describes the interactions between variables with a joint Gaussian that enables fast inference, imputation with confidence intervals, and multiple imputation. The package also provides specialized extensions to handle large datasets (with complexity linear in the number of observations) and streaming datasets (with online imputation). This article describes the underlying methodology and demonstrates how to use the software package. Creator An entity primarily responsible for making the resource Yuxuan Zhao Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v108i04 Publisher An entity responsible for making the resource available Cornell University Date A point or period of time associated with an event in the lifecycle of the resource February 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/3183eb5d652c622be3f38e5dcd2546ef.pdf a33754e10abacaf617353a9084601f7d 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 108 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v108 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 DoubleML: An Object-Oriented Implementation of Double Machine Learning in R Subject The topic of the resource machine learning, causal inference, causal machine learning, R, mlr3, object orientation. Description An account of the resource The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consists of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods. Creator An entity primarily responsible for making the resource Philipp Bach Source A related resource from which the described resource is derived Philipp Bach Publisher An entity responsible for making the resource available University of Hamburg Date A point or period of time associated with an event in the lifecycle of the resource March 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