1 10 12 https://repository.horizon.ac.id/files/original/36564d048426ac63794d162465c381a5.pdf a46a18d9ed9b06c699776a33ad978f66 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 Application of Equal Local Levels to Improve Q-Q Plot Testing Bands with R Package qqconf Subject The topic of the resource : Q-Q plots, equal local levels, Kolmogorov-Smirnov, GWAS, multiple testing, global testing, simultaneous region. Description An account of the resource Quantile-quantile (Q-Q) plots are often difficult to interpret because it is unclear how large the deviation from the theoretical distribution must be to indicate a lack of fit. Most Q-Q plots could benefit from the addition of meaningful global testing bands, but the use of such bands unfortunately remains rare because of the drawbacks of current approaches and packages. These drawbacks include incorrect global type-I error rate, lack of power to detect deviations in the tails of the distribution, relatively slow computation for large data sets, and limited applicability. To solve these problems, we apply the equal local levels global testing method, which we have implemented in the R Package qqconf, a versatile tool to create Q-Q plots and probability-probability (P-P) plots in a wide variety of settings, with simultaneous testing bands rapidly created using recentlydeveloped algorithms. qqconf can easily be used to add global testing bands to Q-Q plots made by other packages. In addition to being quick to compute, these bands have a variety of desirable properties, including accurate global levels, equal sensitivity to deviations in all parts of the null distribution (including the tails), and applicability to a range of null distributions. We illustrate the use of qqconf in several applications: assessing normality of residuals from regression, assessing accuracy of p values, and use of Q-Q plots in genome-wide association studies. Creator An entity primarily responsible for making the resource Eric Weine Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i10 Publisher An entity responsible for making the resource available University of Chicago Date A point or period of time associated with an event in the lifecycle of the resource March 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/8e083b6f62cdc29f405b0c0cdc38073e.pdf f21c7c349aec2db5a362e499226b60ba 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 bootUR: An R Package for Bootstrap Unit Root Tests Subject The topic of the resource bootstrap, R, time series, unit roots Description An account of the resource Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR. The package’s backbone is the popular augmented DickeyFuller test paired with a union of rejections principle, which can be performed directly on single time series or multiple (including panel) time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one’s desired settings. Our parallelized C++ implementation ensures that all unit root tests are scalable to datasets containing many time series. Creator An entity primarily responsible for making the resource Stephan Smeekes Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i12 Publisher An entity responsible for making the resource available Maastricht University Date A point or period of time associated with an event in the lifecycle of the resource March 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/1ecec7d7f753182eceed27c1043fbb5f.pdf 849baf0ead6b66a51b8b0f58716e829d 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 Broken Stick Model for Irregular Longitudinal Data Subject The topic of the resource brokenstick, R, linear mixed model, repeated measures, linear B-spline, personalized estimation, growth curve analysis, critical periods, time-to-time correlation, profile analysis, curve interpolation, multiple imputation, curve matching, two-step method, KasimRaudenbush method Description An account of the resource Many longitudinal studies collect data that have irregular observation times, often requiring the application of linear mixed models with time-varying outcomes. This paper presents an alternative that splits the quantitative analysis into two steps. The first step converts irregularly observed data into a set of repeated measures through the broken stick model. The second step estimates the parameters of scientific interest from the repeated measurements at the subject level. The broken stick model approximates each subject’s trajectory by a series of connected straight lines. The breakpoints, specified by the user, divide the time axis into consecutive intervals common to all subjects. Specification of the model requires just three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: Subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The R package brokenstick v2.5.0 offers tools to calculate, predict, impute and visualize broken stick estimates. The package supports two optimization methods, including options to constrain the variancecovariance matrix of the random effects. We demonstrate six applications of the model: Detection of critical periods, estimation of the time-to-time correlations, profile analysis, curve interpolation, multiple imputation and personalized prediction of future outcomes by curve matching Creator An entity primarily responsible for making the resource Stef van Buuren Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i07 Publisher An entity responsible for making the resource available Netherlands Organization for Applied Scientific Research TNO, Leiden University of Utrecht Date A point or period of time associated with an event in the lifecycle of the resource March 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/737dd3269b42a24900b6ca1f7ee4e15a.pdf aea274aeaa63cadc2dcef3dea08d55db 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 disaggregation: An R Package for Bayesian Spatial Disaggregation Modeling Subject The topic of the resource Bayesian spatial modeling, disaggregation modeling, TMB. Description An account of the resource Disaggregation modeling, or downscaling, has become an important discipline in epidemiology. Surveillance data, aggregated over large regions, is becoming more common, leading to an increasing demand for modeling frameworks that can deal with this data to understand spatial patterns. Disaggregation regression models use response data aggregated over large heterogeneous regions to make predictions at fine-scale over the region by using fine-scale covariates to inform the heterogeneity. This paper presents the R package disaggregation, which provides functionality to streamline the process of running a disaggregation model for fine-scale predictions. Creator An entity primarily responsible for making the resource Anita K. Nandi Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i11 Publisher An entity responsible for making the resource available University of Oxford Date A point or period of time associated with an event in the lifecycle of the resource March 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/cffd91ad13ea022403b282cc9964a235.pdf 43f2c85c2f9c20e4317ab33dd3e0699a 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 drda: An R Package for Dose-Response Data Analysis Using Logistic Functions Subject The topic of the resource curve fitting, dose-response, drug sensitivity, logistic function, nonlinear regression. Description An account of the resource Analysis of dose-response data is an important step in many scientific disciplines, including but not limited to pharmacology, toxicology, and epidemiology. The R package drda is designed to facilitate the analysis of dose-response data by implementing efficient and accurate functions with a familiar interface. With drda it is possible to fit models by the method of least squares, perform goodness-of-fit tests, and conduct model selection. Compared to other similar packages, drda provides in general more accurate estimates in the least-squares sense. This result is achieved by a smart choice of the starting point in the optimization algorithm and by implementing the Newton method with a trust region with analytical gradients and Hessian matrices. In this article, drda is presented through the description of its methodological components and examples of its user-friendly functions. Performance is evaluated using both synthetic data and a real, large-scale drug sensitivity screening dataset Creator An entity primarily responsible for making the resource Alina Malyutina Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i04 Publisher An entity responsible for making the resource available University of Helsinki Date A point or period of time associated with an event in the lifecycle of the resource March 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/8cebf5edc7acd7e1673bb366c4cba21f.pdf 13078abd1c3955833a1e220f5c377cb7 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 Elastic Net Regularization Paths for All Generalized Linear Models Subject The topic of the resource : lasso, elastic net, ℓ1 penalty, regularization path, coordinate descent, generalized linear models, survival, Cox mode Description An account of the resource The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop] data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models Creator An entity primarily responsible for making the resource J. Kenneth Tay Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i01 Publisher An entity responsible for making the resource available Stanford University Date A point or period of time associated with an event in the lifecycle of the resource March 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/3ef9719026e2035a144b1ded9d095455.pdf a058e2349ad29903e1ea06ac483630a3 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection Subject The topic of the resource change-point detection, constrained inference, maximum likelihood inference, dynamic programming, robust losses Description An account of the resource In a world with data that change rapidly and abruptly, it is important to detect those changes accurately. In this paper we describe an R package implementing a generalized version of an algorithm recently proposed by Hocking, Rigaill, Fearnhead, and Bourque (2020) for penalized maximum likelihood inference of constrained multiple change-point models. This algorithm can be used to pinpoint the precise locations of abrupt changes in large data sequences. There are many application domains for such models, such as medicine, neuroscience or genomics. Often, practitioners have prior knowledge about the changes they are looking for. For example in genomic data, biologists sometimes expect peaks: up changes followed by down changes. Taking advantage of such prior information can substantially improve the accuracy with which we can detect and estimate changes. Hocking et al. (2020) described a graph framework to encode many examples of such prior information and a generic algorithm to infer the optimal model parameters, but implemented the algorithm for just a single scenario. We present the gfpop package that implements the algorithm in a generic manner in R/C++. gfpop works for a user-defined graph that can encode prior assumptions about the types of changes that are possible and implements several loss functions (Gauss, Poisson, binomial, biweight, and Huber). We then illustrate the use of gfpop on isotonic simulations and several applications in biology. For a number of graphs the algorithm runs in a matter of seconds or minutes for 105 data points. Creator An entity primarily responsible for making the resource Vincent Runge Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i06 Publisher An entity responsible for making the resource available Université d’Évry Date A point or period of time associated with an event in the lifecycle of the resource March 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/61302d54d7e2ff21bc7c2113bc13140b.pdf 75a94fd7443638720241987c9709867d 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 intRinsic: An R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset Subject The topic of the resource nearest neighbors, likelihood-based method, heterogeneous intrinsic dimension, Bayesian mixture model, R Description An account of the resource This article illustrates intRinsic, an R package that implements novel state-of-the-art likelihood-based estimators of the intrinsic dimension of a dataset, an essential quantity for most dimensionality reduction techniques. In order to make these novel estimators easily accessible, the package contains a small number of high-level functions that rely on a broader set of efficient, low-level routines. Generally speaking, intRinsic encompasses models that fall into two categories: homogeneous and heterogeneous intrinsic dimension estimators. The first category contains the two nearest neighbors estimator, a method derived from the distributional properties of the ratios of the distances between each data point and its first two closest neighbors. The functions dedicated to this method carry out inference under both the frequentist and Bayesian frameworks. In the second category, we find the heterogeneous intrinsic dimension algorithm, a Bayesian mixture model for which an efficient Gibbs sampler is implemented. After presenting the theoretical background, we demonstrate the performance of the models on simulated datasets. This way, we can facilitate the exposition by immediately assessing the validity of the results. Then, we employ the package to study the intrinsic dimension of the Alon dataset, obtained from a famous microarray experiment. Finally, we show how the estimation of homogeneous and heterogeneous intrinsic dimensions allows us to gain valuable insights into the topological structure of a dataset. Creator An entity primarily responsible for making the resource Francesco Denti Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i09 Publisher An entity responsible for making the resource available Università Cattolica del Sacro Cuore Date A point or period of time associated with an event in the lifecycle of the resource March 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/55291d0e8f377b653896e838c7ae9e29.pdf b88acf4b1ac84f98030dcc2e0197f9a2 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 MLGL: An R Package Implementing Correlated Variable Selection by Hierarchical Clustering and Group-Lasso Subject The topic of the resource penalized regression, correlated variables, hierarchical clustering, group selection, R Description An account of the resource The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high-dimensional data. A sparsity assumption is made that postulates that only a few variables are relevant for predicting the response variable. In this context, the performance of classical Lasso-based approaches strongly deteriorates as the redundancy increases. The proposed approach combines variables aggregation and selection in order to improve interpretability and performance. First, a hierarchical clustering procedure provides at each level a partition of the variables into groups. Then, the set of groups of variables from the different levels of the hierarchy is given as input to group-Lasso, with weights adapted to the structure of the hierarchy. At this step, group-Lasso outputs sets of candidate groups of variables for each value of the regularization parameter. The versatility offered by package MLGL to choose groups at different levels of the hierarchy a priori induces a high computational complexity. MLGL, however, exploits the structure of the hierarchy and the weights used in group-Lasso to greatly reduce the final time cost. The final choice of the regularization parameter – and therefore the final choice of groups – is made by a multiple hierarchical testing procedure. Creator An entity primarily responsible for making the resource Quentin Grimonprez Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i03 Publisher An entity responsible for making the resource available Inria Lille-Nord Europe Date A point or period of time associated with an event in the lifecycle of the resource March 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/839f40ce7ee7ef3db4fc5f210366cd24.pdf 96e2fb4fbbe4019bdd34bf89e9b70eba 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 106 Tahun 2023 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v106 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 netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods Subject The topic of the resource network meta-analysis, R, inconsistency, component network meta-analysis. Description An account of the resource Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta Creator An entity primarily responsible for making the resource Sara Balduzzi Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v106i02 Publisher An entity responsible for making the resource available Universität Freiburg Date A point or period of time associated with an event in the lifecycle of the resource March 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