1 10 10 https://repository.horizon.ac.id/files/original/67b1ed8faf51bf0706cc08b83032a46b.pdf 27b771805237807d21d3e930a5c703fb 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. 113 (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 BayesMortalityPlus: A Package in R for Bayesian Mortality Modeling Description An account of the resource The BayesMortalityPlus package provides a framework for modeling and predicting mortality data. The package includes tools for the construction of life tables based on Heligman-Pollard laws, and also on dynamic linear smoothers. Flexibility is available in terms of modeling so that the response variable may be modeled as Poisson, binomial or Gaussian. If temporal data is available, the package provides a Bayesian implementation for the well-known Lee-Carter model that allows for estimation, projection of mortality over time, and assessment of uncertainty of any linear or nonlinear function of parameters such as life expectancy. Illustrations are considered to show the capability of the proposed package to model mortality data. Creator An entity primarily responsible for making the resource Lucas M. F. Silva, Luiz F. V. Figueiredo, Viviana G. R. Lobo, Thais C. O. Fonseca, Mariane B. Alves Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i09 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 JULI 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/13c04200fa70daf6045543daff0f2f09.pdf d15bf794ed97dfea13e0cb9d2f7272cb 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. 113 (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 BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis Description An account of the resource Modern applications of survival analysis increasingly involve time-dependent covariates.The Python package BoXHED2.0 (Boosted eXact Hazard Estimator with Dynamic covariates) is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-censoring, including recurring events and competing risks. BoXHED2.0 is also scalable to the point of being on the same order of speed as parametric boosted survival models, in part because its core is written in C++ and it also supports the use of GPUs and multicore CPUs. BoXHED2.0 is available from PyPI and also from https://github.com/BoXHED. Creator An entity primarily responsible for making the resource Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi, Donald K. K. Lee Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i03 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 28 JULI 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/10180fabbb8d7719ebbfa0a6264d93e5.pdf 03a7fb31465c38cf6239543d0ec87e25 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. 113 (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 dame-flame: A Python Package Providing Fast Interpretable Matching for Causal Inference Description An account of the resource dame-flame is a Python package for performing matching for observational causal inference on datasets containing discrete covariates. This package implements the dynamic almost matching exactly (DAME) and fast, large-scale almost matching exactly (FLAME) algorithms, which match treatment and control units on subsets of the covariates. The resulting matched groups are interpretable, because the matches are made directly on covariates, and high-quality, because machine learning is used to determine which covariates are important to match on instead of human inputs. The package provides several adjustable parameters to adapt the algorithms to specific applications, and can calculate treatment effects after matching. The most recent source code of the implementation is available at https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package. Creator An entity primarily responsible for making the resource Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i02 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 31 JULI 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/2463abe88799fa0e2c36f78735f4068c.pdf f97e4d74fb8e87ec20207ce641856509 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. 113 (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 Estimating Spatial Dynamic Panel Data Models with Unobserved Common Factors in Stata Description An account of the resource This article introduces the spxtivdfreg package in Stata, which implements a general instrumental variables (IV) approach for estimating dynamic spatial panel data models with unobserved common factors or interactive effects, when the number of both cross-sectional and time series observations is large. The estimator has been developed in a recent paper by Cui, Sarafidis, and Yamagata (2023). The underlying idea is to project out the common factors from exogenous covariates using principal components analysis, and to run IV regression in both of two stages, using defactored covariates (and their spatial counterparts) as instruments. The resulting two-stage IV estimator is valid for models with homogeneous slope coefficients, and has several advantages relative to existing popular approaches. In addition, the spxtivdfreg package allows estimation of short-run and long-run direct and indirect effects, as well as total effects, accounting for the cumulative effects over time and across space. Standard errors for such effects are computed using the Delta method. Last, the spxtivdfreg package allows for heterogeneous slope coefficients, as in Chen, Cui, Sarafidis, and Yamagata (2025). In particular, we construct a "mean group" IV estimator, which involves averaging first-step IV estimates of individual-specific slope coefficients. Creator An entity primarily responsible for making the resource Sebastian Kripfganz, Vasilis Sarafidis Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i06 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 28 JULI 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/cd168efb5adef8b17df9997cfc3626a4.pdf 67805b4e79e3339fc19da413b911d56a 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. 113 (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 pymle: A Python Package for Maximum Likelihood Estimation and Simulation of Stochastic Differential Equations Description An account of the resource This paper introduces the object-oriented Python package pymle, which provides core functionality for maximum likelihood estimation and simulation of univariate stochastic differential equations. The package supports maximum likelihood estimation using Euler, Elerian, Ozaki, Shoji-Ozaki, Hermite polynomial, and Kessler density approximations, as well as a recently proposed continuous-time Markov chain approximation scheme. Exact maximum likelihood estimation is also provided when available. The framework supports estimation and simulation for 21 stochastic differential equations models at the time of writing, and its object oriented design facilitates easy extensions to new models and approximation methods. Creator An entity primarily responsible for making the resource Justin L. Kirkby, Dang H. Nguyen, Duy Nguyen, Nhu Nguyen Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i04 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 6 AUGUSTUS 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/66afa4675485b512d2b838a2691fa82c.pdf 90515192889ceed11a6f2b0b4c318d18 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. 113 (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 scpi: Uncertainty Quantification for Synthetic Control Methods Description An account of the resource The synthetic control method offers a way to quantify the effect of an intervention using weighted averages of untreated units to approximate the counterfactual outcome that the treated unit(s) would have experienced in the absence of the intervention. This method is useful for program evaluation and causal inference in observational studies. We introduce the software package scpi for prediction and inference using synthetic controls, implemented in Python, R, and Stata. For point estimation or prediction of treatment effects, the package offers an array of (possibly penalized) approaches leveraging the latest optimization methods. For uncertainty quantification, the package offers the prediction interval methods introduced by Cattaneo, Feng, and Titiunik (2021) and Cattaneo, Feng, Palomba, and Titiunik (2025b). The paper includes numerical illustrations and a comparison with other synthetic control software. Creator An entity primarily responsible for making the resource Matias Cattaneo, Yingjie Feng, Filippo Palomba, Rocío Titiunik Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i01 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 JULI 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/9e5d588329d979f3615b8829a4b9a67b.pdf 77f9733b590abed52a43df8bb09436b9 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. 113 (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 StatisticalProcessMonitoring.jl: A General Framework for Statistical Process Monitoring in Julia Description An account of the resource Statistical process monitoring (SPM) control charts are widely used for monitoring the stability of sequential processes. Currently, there is no open-source software which provides a general and extensible implementation of control charts. StatisticalProcessMonitoring.jl is a novel Julia package which aims at addressing this gap, offering support for monitoring various type of data, such as univariate and multivariate observations, partially-observed data streams, and profiles. The package introduces an extensible SPM framework, allowing users to seamlessly design control charts for structured data types using the existing implementation. By introducing a flexible implementation of control charts, StatisticalProcessMonitoring.jl provides fully-automated and efficient algorithms for determining control limits and tuning control chart hyperparameters. These algorithms can accommodate various commonly-used performance metrics based on the run length distribution. The package further leverages existing packages in the Julia ecosystem to offer users a range of optimization and plotting functionalities. Creator An entity primarily responsible for making the resource Daniele Zago Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i07 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 28 JULI 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/e77f929d650c2487f69f3de4ff861d5e.pdf 77f9733b590abed52a43df8bb09436b9 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. 113 (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 StepMix: A Python Package for Pseudo-Likelihood Estimation of Generalized Mixture Models with External Variables Subject The topic of the resource StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper. Description An account of the resource StepMix is an open-source Python package for the pseudo-likelihood estimation (one-, two- and three-step approaches) of generalized finite mixture models (latent profile and latent class analysis) with external variables (covariates and distal outcomes). In many applications in social sciences, the main objective is not only to cluster individuals into latent classes, but also to use these classes to develop more complex statistical models. These models generally divide into a measurement model that relates the latent classes to observed indicators, and a structural model that relates covariates and outcome variables to the latent classes. The measurement and structural models can be estimated jointly using the so-called one-step approach or sequentially using stepwise methods, which present significant advantages for practitioners regarding the interpretability of the estimated latent classes. In addition to the one-step approach, StepMix implements the most important stepwise estimation methods from the literature, including the bias-adjusted three-step methods with Bolk-Croon-Hagenaars and maximum likelihood corrections and the more recent two-step approach. These pseudo-likelihood estimators are presented in this paper under a unified framework as specific expectation-maximization subroutines. To facilitate and promote their adoption among the data science community, StepMix follows the object-oriented design of the scikit-learn library and provides an additional R wrapper. Creator An entity primarily responsible for making the resource Sacha Morin, Robin Legault, Félix Laliberté, Zsuzsa Bakk, Charles-Édouard Giguère, Roxane de la Sablonnière, Éric Lacourse Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i08 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 JULI 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/7a9b1bbe42431a699ae639df4dfe13e3.pdf 95d3096357b109e5185c7d9957cef352 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. 113 (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 stopp: An R Package for Spatio-Temporal Point Pattern Analysis Description An account of the resource stopp is a novel R package specifically designed for the analysis of spatio-temporal point patterns which might have occurred in a subset of the Euclidean space or on some specific linear network, such as roads of a city. It represents the first package providing a comprehensive modeling framework for spatio-temporal Poisson point processes. While many specialized models exist in the scientific literature for analyzing complex spatio-temporal point patterns, we address the lack of general software for comparing simpler alternative models and their goodness of fit. The package's main functionalities include modeling and diagnostics, together with exploratory analysis tools and the simulation of point processes. A particular focus is given to local first-order and second-order characteristics. The package aggregates existing methods within one coherent framework, including those we proposed in recent papers, and it aims to welcome many further proposals and extensions from the R community. Creator An entity primarily responsible for making the resource Nicoletta D'Angelo, Giada Adelfio Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i10 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 JULI 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/6bf7135e412432d2953a4a484f584660.pdf ff985153c645c1b33809e9578c0ad248 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. 113 (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 SURVEYHLM: A SAS Macro for Multilevel Analysis with Large-Scale Educational Assessment Data Description An account of the resource Special techniques must be considered during analysis of large-scale educational assessment (LSA) data. In this regard, many software packages are available to support researchers conducting secondary analyses. However, the software packages available for multilevel analyses are somewhat limited and usually contain only a few of the required techniques. In this article, we review the technical details of LSA studies and describe our comparison of software for multilevel analyses by questioning the extent to which these packages take these technical details into account. In accordance with our findings from this comparison, we developed a SAS macro for multilevel analyses of LSA data that meets all technical requirements. The macro SURVEYHLM fits multilevel models with LSA datasets. SURVEYHLM can handle up to three levels. It can fit different correlation structures for the random components and use plausible values as response variables, and the responses do not necessarily need to be normally distributed. Weights can be specified on levels 1, 2 and 3. Scaling of the level-specific weights is possible, and standard errors can be based on a sandwich estimator or calculated with either the jackknife replication technique or through user-supplied replication weights. Examples of applications are given Creator An entity primarily responsible for making the resource Daniel Kasper, Katrin Schulz-Heidorf, Knut Schwippert Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v113i05 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 JULI 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