1 10 10 https://repository.horizon.ac.id/files/original/85ae6887434d7ac91435bb8d08516493.pdf 0814198178f49afb042d1400929278a8 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 Additive Bayesian Network Modeling with the R Package abn Subject The topic of the resource : structure learning, graphical models, greedy search, exact search, scoring algorithm, GLM, graph theory Description An account of the resource The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or information theoretic formulations of generalized linear models. It is equipped with exact search and greedy search algorithms to select the best network, and supports continuous, discrete and count data in the same model and input of prior knowledge at a structural level. The Bayesian implementation supports random effects to control for one-layer clustering. In this paper, we give an overview of the methodology and illustrate the package’s functionality using a veterinary dataset concerned with respiratory diseases in commercial swine production. Creator An entity primarily responsible for making the resource Gilles Kratzer Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i08 Publisher An entity responsible for making the resource available University of Zurich Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/95f78ef9fb306088859c40a8c65cc052.pdf 1be08aaf2d341d078e2d5f1f4dcf26da 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG Subject The topic of the resource Bayesian networks, dynamic Bayesian networks, structure learning, Bayesian inference, MCMC, R Description An account of the resource The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. In the first step, we define a reduced search space by means of the PC algorithm or based on prior knowledge. In the second step, an iterative order MCMC scheme proceeds to optimize the restricted search space and estimate the MAP graph. Sampling from the posterior distribution is implemented using either order or partition MCMC. The models and algorithms can handle both discrete and continuous data. The BiDAG package also provides an implementation of MCMC schemes for structure learning and sampling of dynamic Bayesian networks. Creator An entity primarily responsible for making the resource Polina Suter Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i09 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 January 2023 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/266ddece8c4d3a997d2460d5e43956f7.pdf 7203451caeb6d046381b7a98d5ca9721 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values Subject The topic of the resource cglasso, conditional Gaussian graphical models, glasso, high-dimensionality, sparsity, censoring, missing data. Description An account of the resource Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an ℓ1-penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two ℓ1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data. Creator An entity primarily responsible for making the resource Luigi Augugliaro Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i01 Publisher An entity responsible for making the resource available University of Palermo Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/3a863b881f9895f06f484e82e16b142d.pdf 77d4da404ccdf5de58180b21e9aca899 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 deepregression: A Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression Subject The topic of the resource additive predictors, deep learning, effect decomposition, orthogonal complement, penalization, smoothing. Description An account of the resource In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library TensorFlow for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as mgcv. The package’s modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models. Creator An entity primarily responsible for making the resource David Rügamer Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i02 Publisher An entity responsible for making the resource available LMU Munich Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/846983ef8e1d7f6ce172cd3b0a7e63c3.pdf 5fd8c7d34484f51050ede29ab5bcd98a 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 ergm 4: New Features for Analyzing Exponential-Family Random Graph Models Subject The topic of the resource : statnet, ERGM, exponential-family random graph models, valued networks. Description An account of the resource The ergm package supports the statistical analysis and simulation of network data. It anchors the statnet suite of packages for network analysis in R introduced in a special issue in Journal of Statistical Software in 2008. This article provides an overview of the new functionality in the 2021 release of ergm version 4. These include more flexible handling of nodal covariates, term operators that extend and simplify model specification, new models for networks with valued edges, improved handling of constraints on the sample space of networks, and estimation with missing edge data. We also identify the new packages in the statnet suite that extend ergm’s functionality to other network data types and structural features and the robust set of online resources that support the statnet development process and applications. Creator An entity primarily responsible for making the resource Pavel N. Krivitsky Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i06 Publisher An entity responsible for making the resource available University of New South Wales Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/2ba8dee09eca5fe459be16896c6e9d68.pdf 1c3ce97ac7ac10f6ec952dae79233c83 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations Subject The topic of the resource statistical computing, statistical graphics, data science, data visualization, tidyverse, data pipeline, R. Description An account of the resource Despite the large body of research on missing value distributions and imputation, there is comparatively little literature with a focus on how to make it easy to handle, explore, and impute missing values in data. This paper addresses this gap. The new methodology builds upon tidy data principles, with the goal of integrating missing value handling as a key part of data analysis workflows. We define a new data structure, and a suite of new operations. Together, these provide a connected framework for handling, exploring, and imputing missing values. These methods are available in the R package naniar. Creator An entity primarily responsible for making the resource Nicholas Tierney Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i07 Publisher An entity responsible for making the resource available Monash University, Telethon Kids Institute Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/f67756eb6293cf988114e095730efebe.pdf bcee0b8576c7aefc8a0e45d7e953f197 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations Subject The topic of the resource logit, utility, preference, willingness to pay, discrete choice models, R, maximum likelihood estimation. Description An account of the resource This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with “preference space” or “willingness-to-pay (WTP) space” parameterizations, allowing for the direct estimation of marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in mixed logit models. The paper provides a discussion of some of the implications of each utility parameterization for WTP estimates. It also highlights some of the design features that enable logitr’s performant estimation speed and includes a benchmarking exercise with similar packages. Finally, the paper highlights additional features that are designed specifically for WTP space models, including a consistent user interface for specifying models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating models with non-convex log-likelihood functions. Creator An entity primarily responsible for making the resource John Paul Helveston Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i10 Publisher An entity responsible for making the resource available George Washington University Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/d55d7acf2fcac677b2f472f25843fc9e.pdf a7284b22db896005f3b99c8a09c3afb9 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg Subject The topic of the resource event plot, frailty, joint model, mean cumulative function, simulation, survival data. Description An account of the resource Recurrent event analyses have found a wide range of applications in biomedicine, public health, and engineering, among others, where study subjects may experience a sequence of event of interest during follow-up. The R package reReg offers a comprehensive collection of practical and easy-to-use tools for regression analysis of recurrent events, possibly with the presence of an informative terminal event. The regression framework is a general scalechange model which encompasses the popular Cox-type model, the accelerated rate model, and the accelerated mean model as special cases. Informative censoring is accommodated through a subject-specific frailty without any need for parametric specification. Different regression models are allowed for the recurrent event process and the terminal event. Also included are visualization and simulation tools Creator An entity primarily responsible for making the resource Sy Han Chiou Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i05 Publisher An entity responsible for making the resource available https://www.jstatsoft.org/article/view/v105i05 Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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/08fcf2d2acbefd05bfd28f59d551a2b2.pdf d414f13177f5d47d030a395ada2d2378 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 spsurvey: Spatial Sampling Design and Analysis in R Subject The topic of the resource design-based inference, generalized random-tessellation stratified algorithm, HorvitzThompson, inclusion probability, spatial balance, variance estimation. Description An account of the resource spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts() function flexibly accommodates several sampling design features, including stratification, varying inclusion probabilities, legacy (or historical) sites, minimum distances between sites, and two options for replacement sites. spsurvey also provides a suite of data analysis options, including categorical variable analysis (cat_analysis()), continuous variable analysis (cont_analysis()), relative risk analysis (relrisk_analysis()), attributable risk analysis (attrisk_analysis()), difference in risk analysis (diffrisk_analysis()), change analysis (change_analysis()), and trend analysis (trend_analysis()). In this manuscript, we first provide background for the GRTS algorithm and the analysis approaches and then show how to implement them in spsurvey. We find that the spatially balanced GRTS algorithm yields more precise parameter estimates than simple random sampling, which ignores spatial information. Creator An entity primarily responsible for making the resource Michael Dumelle Source A related resource from which the described resource is derived http://jstatsoft.org/article/view/v105i03 Publisher An entity responsible for making the resource available United States Environmental Protection Agency Date A point or period of time associated with an event in the lifecycle of the resource January 2022 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/bcb4f40e7fa918a703bda23f9e0d4e0e.pdf be49c1931865e153b3510b19d3b11885 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 105 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v105 Date A point or period of time associated with an event in the lifecycle of the resource 2023 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 umpdiff: A Python Library for Statistical Inference of Jump-Diffusion Processes in Observational or Experimental Data Sets Subject The topic of the resource stochastic differential equations, jump-diffusion processes, Kramers-Moyal expansion, Kramers-Moyal coefficients, Python. Description An account of the resource We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute a set of non-parametric estimators of all contributions composing a jump-diffusion process, namely the drift, the diffusion, and the stochastic jump strengths. Having a set of measurements from a jump-diffusion process, jumpdiff is able to retrieve the evolution equation producing data series statistically equivalent to the series of measurements. The back-end calculations are based on second-order corrections of the conditional moments expressed from the series of Kramers-Moyal coefficients. Additionally, the library is also able to test if stochastic jump contributions are present in the dynamics underlying a set of measurements. Finally, we introduce a simple iterative method for deriving secondorder corrections of any Kramers-Moyal coefficient. Creator An entity primarily responsible for making the resource Leonardo Rydin Gorjão NMBU Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v105i04 Publisher An entity responsible for making the resource available Forschungszentrum Jülich, University of Cologne Date A point or period of time associated with an event in the lifecycle of the resource January 2023 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