1 10 12 https://repository.horizon.ac.id/files/original/1c6e0ce51dc92b52397bca4bf3c49790.pdf 779e194a7efb3db33608fa5770a18c5a 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 Fast Kernel Smoothing in R with Applications to Projection Pursuit Subject The topic of the resource kernel smoothing, nonparametric, density estimation, regression, projection pursuit, independent component analysis, R Description An account of the resource This paper introduces the R package FKSUM, which offers fast and exact evaluation of univariate kernel smoothers. The main kernel computations are implemented in C++, and are wrapped in simple, intuitive and versatile R functions. The fast kernel computations are based on recursive expressions involving the order statistics, which allows for exact evaluation of kernel smoothers at all sample points in log-linear time. In addition to general purpose kernel smoothing functions, the package offers purpose built and readyto-use implementations of popular kernel-type estimators. On top of these basic smoothing problems, this paper focuses on projection pursuit problems in which the projection index is based on kernel-type estimators of functionals of the projected density. Creator An entity primarily responsible for making the resource David P. Hofmeyr Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i03 Publisher An entity responsible for making the resource available Stellenbosch University 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/af8f6cc28992002cb4f33e83a470f617.pdf 4788c2285deafb1cad4bca56b97bbd11 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 tidypaleo: Visualizing Paleoenvironmental Archives Using ggplot2 Subject The topic of the resource : plotting software, stratigraphic diagrams, R, ggplot2. Description An account of the resource This paper presents the tidypaleo package for R, which enables high-quality reproducible visualizations of time-stratigraphic multivariate data that is common to several disciplines of the natural sciences. Rather than introduce new plotting functions, the tidypaleo package defines several orthogonal components of the ggplot2 package that, when combined, enable most types of stratigraphic diagrams to be created. We do so by conceptualizing multi-parameter data as a series of measurements (rows) with attributes (columns), enabling the use of the ggplot2 facet mechanism to display multi-parameter data. The orthogonal components include (1) scales that represent relative abundance and concentration values, (2) geometries that are commonly used in paleoenvironmental diagrams created elsewhere, (3) facets that correctly assign scales and sizes to panels representing multiple parameters, and (4) theme elements that enable tidypaleo to create elegant graphics. Collectively, this approach demonstrates the efficacy of a minimal ggplot2 wrapper to create domain-specific plots. Creator An entity primarily responsible for making the resource Ian S. Spooner Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i07 Publisher An entity responsible for making the resource available Acadia University 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/dc73f145dcf268e3a4e61def32934cdd.pdf c9fe2de17ddf67d503b334dc91426589 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student-t Probabilities with Low-Rank Methods in R Subject The topic of the resource excursion sets, high dimensions, multivariate normal, multivariate Student-t, tlrmvnmvt Description An account of the resource This paper introduces the usage and performance of the R package tlrmvnmvt, aimed at computing high-dimensional multivariate normal and Student-t probabilities. The package implements the tile-low-rank methods with block reordering and the separationof-variable methods with univariate reordering. The performance is compared with two other state-of-the-art R packages, namely the mvtnorm and the TruncatedNormal packages. Our package has the best scalability and is likely to be the only option in thousands of dimensions. However, for applications with high accuracy requirements, the TruncatedNormal package is more suitable. As an application example, we show that the excursion sets of a latent Gaussian random field can be computed with the tlrmvnmvt package without any model approximation and hence, the accuracy of the produced excursion sets is improved. Creator An entity primarily responsible for making the resource Jian Cao Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i04 Publisher An entity responsible for making the resource available King Abdullah University of Science and Technology 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/1d05f4c9c6c815f6e11dacb529159651.pdf 88166bf659c9279733542bc3c631f78c 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 TransModel: An R Package for Linear Transformation Model with Censored Data Subject The topic of the resource transformation model, right censored data, R package. Description An account of the resource Linear transformation models, including the proportional hazards model and proportional odds model, under right censoring were discussed by Chen, Jin, and Ying (2002). The asymptotic variance of the estimator they proposed has a closed form and can be obtained easily by plug-in rules, which improves the computational efficiency. We develop an R package TransModel based on Chen’s approach. The detailed usage of the package is discussed, and the function is applied to the Veterans’ Administration lung cancer data Creator An entity primarily responsible for making the resource Jie Zhou Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i09 Publisher An entity responsible for making the resource available University of South Carolina 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/90fef40c368ebcab29d567af945263d3.pdf 9ecc06fb2b7c2f65bcd81d7537a05b89 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R Jun Woo, Jinhua Wang Subject The topic of the resource supervised learning, Boltzmann machine, naive Bayes, discriminant analysis, R. Description An account of the resource Non-regression-based inferences, such as discriminant analysis, can account for the effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a comprehensive supervised learning of the association between a large number of categorical predictors and multi-level response variables. Its basic underlying statistical model is a collection of (fully visible) Boltzmann machines inferred for each distinct response level. The algorithm reduces to the naive Bayes learner when interaction is ignored. We illustrate example use cases for various scenarios, ranging from modeling of a relatively small set of factors with heterogeneous levels to those with hundreds or more predictors with uniform levels such as image or genomic data. We show how bbl explicitly quantifies the extra power provided by interactions via higher predictive performance of the model. In comparison to deep learning-based methods such as restricted Boltzmann machines, bbl-trained models can be interpreted directly via their bias and interaction parameters. Creator An entity primarily responsible for making the resource Jun Woo Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i05 Publisher An entity responsible for making the resource available University of Minnesota, Minneapolis 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/87da2bb173ef02a62ccaa97f2b3778c5.pdf 947efb886803f2139d1378c655351f89 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 lpdensity: Local Polynomial Density Estimation and Inference Subject The topic of the resource : kernel-based nonparametrics, local polynomial, density estimation, bandwidth selection, bias correction, robust inference, boundary carpentry, R, Stata Description An account of the resource Density estimation and inference methods are widely used in empirical work. When the underlying distribution has compact support, conventional kernel-based density estimators are no longer consistent near or at the boundary because of their well-known boundary bias. Alternative smoothing methods are available to handle boundary points in density estimation, but they all require additional tuning parameter choices or other typically ad hoc modifications depending on the evaluation point and/or approach considered. This article discusses the R and Stata package lpdensity implementing a novel local polynomial density estimator proposed and studied in Cattaneo, Jansson, and Ma (2020, 2022), which is boundary adaptive and involves only one tuning parameter. The methods implemented also cover local polynomial estimation of the cumulative distribution function and density derivatives. In addition to point estimation and graphical procedures, the package offers consistent variance estimators, mean squared error optimal bandwidth selection, robust bias-corrected inference, and confidence bands construction, among other features. A comparison with other density estimation packages available in R using a Monte Carlo experiment is provided. Creator An entity primarily responsible for making the resource Matias D. Cattaneo Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i02 Publisher An entity responsible for making the resource available Princeton University 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/2d81059115f8b2000f24ff850311a665.pdf bee179e0620354a1f091f545a035b647 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 The poolr Package for Combining Independent and Dependent p Values Subject The topic of the resource combining p values, dependent p values, R Description An account of the resource The poolr package provides an implementation of a variety of methods for pooling (i.e., combining) p values, including Fisher’s method, Stouffer’s method, the inverse chisquare method, the binomial test, the Bonferroni method, and Tippett’s method. More importantly, the methods can be adjusted to account for dependence among the tests from which the p values have been derived assuming multivariate normality among the test statistics. All methods can be adjusted based on an estimate of the effective number of tests or by using an empirically-derived null distribution based on pseudo replicates that mimics a proper permutation test. For the Fisher, Stouffer, and inverse chi-square methods, the test statistics can also be directly generalized to account for dependence, leading to Brown’s method, Strube’s method, and the generalized inverse chi-square method. In this paper, we describe the various methods, discuss their implementation in the package, illustrate their use based on several examples, and compare the poolr package with several other packages that can be used to combine p value Creator An entity primarily responsible for making the resource Ozan Cinar Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i01 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 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/ce1a0a5c322c2798d6ac10d5ac9202e3.pdf 0032cb07de151ed8ec1474610f1f7818 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 missSBM: An R Package for Handling Missing Values in the Stochastic Block Model Subject The topic of the resource : network, missing data, stochastic block model Description An account of the resource The stochastic block model is a popular probabilistic model for random graphs. It is commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a partially observed network, it is important to consider the underlying process that generates the missing values, otherwise the inference may be biased. This paper presents missSBM, an R package that fits stochastic block models when the network is partially observed, i.e., the adjacency matrix contains not only 1s or 0s encoding the presence or absence of edges, but also NAs encoding the missing information between pairs of nodes. This package implements a set of algorithms to adjust the binary stochastic block model, possibly in the presence of external covariates, by performing variational inference suitable for several observation processes. Our implementation automatically explores different block numbers to select the most relevant model according to the integrated classification likelihood criterion. The integrated classification likelihood criterion can also help determine which observation process best fits a given dataset. Finally, missSBM can be used to perform imputation of missing entries in the adjacency matrix. We illustrate the package on a network dataset consisting of interactions between political blogs sampled during the 2007 French presidential election Creator An entity primarily responsible for making the resource Pierre Barbillon Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i12 Publisher An entity responsible for making the resource available University Paris-Saclay, AgroParisTech, INRAE 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/eee530dc0e5b8caeb9429000dbaef996.pdf 66e4dfcb9ecb4be39c45e7b8b4a39f0c 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 The JuliaConnectoR: A Functionally-Oriented Interface for Integrating Julia in R Subject The topic of the resource : language bridge, R, Julia, deep learning, neural ordinary differential equations. Description An account of the resource Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the Comprehensive R Archive Network (CRAN), the official R package repository, and from GitHub (https://github. com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we decided to base communication between R and Julia on the transmission control protocol, using an optimized binary format for exchanging data. Our package also specifically contains features that allow for a convenient interactive use in R. This makes it easy to develop R extensions with Julia or to simply call functionality from Julia packages in R. Interacting with Julia objects and calling Julia functions becomes user-friendly, as Julia functions and variables are made directly available as objects in the R workspace. We illustrate the further features of our package with code examples, and also discuss advantages over the two alternative packages JuliaCall and XRJulia. Finally, we demonstrate the usage of the package with a more extensive example for employing neural ordinary differential equations, a recent deep learning technique that has received much attention. This example also provides more general guidance for integrating deep learning techniques from Julia into R. Creator An entity primarily responsible for making the resource Stefan Lenz Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i06 Publisher An entity responsible for making the resource available University of Freiburg 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/17b52ee97041dfc5f83649b931737295.pdf 36867599850a6a53f5d12616a6d3784f 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 101 Tahun 2022 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v101 Date A point or period of time associated with an event in the lifecycle of the resource 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 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 Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data Subject The topic of the resource : dynamic programming, optimal changepoint detection, peak detection, genomic data, R Description An account of the resource We describe a new algorithm and R package for peak detection in genomic data sets using constrained changepoint models. These detect changes from background to peak regions by imposing the constraint that the mean should alternately increase then decrease. An existing algorithm for this problem exists, and gives state-of-the-art accuracy results, but it is computationally expensive when the number of changes is large. We propose a dynamic programming algorithm that jointly estimates the number of peaks and their locations by minimizing a cost function which consists of a data fitting term and a penalty for each changepoint. Empirically this algorithm has a cost that is O(N log(N)) for analyzing data of length N. We also propose a sequential search algorithm that finds the best solution with K segments in O(log(K)N log(N)) time, which is much faster than the previous O(KN log(N)) algorithm. We show that our disk-based implementation in the PeakSegDisk R package can be used to quickly compute constrained optimal models with many changepoints, which are needed to analyze typical genomic data sets that have tens of millions of observations. Creator An entity primarily responsible for making the resource Toby Dylan Hocking Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v101i10 Publisher An entity responsible for making the resource available Northern Arizona University 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