2 10 16 https://repository.horizon.ac.id/files/original/09b4cc774fc89e4330454faffc742bbe.pdf 91b6e4ad72e5ad8faf797308ba93be7e 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 Patch-Wise Adaptive Weights Smoothing in R Subject The topic of the resource image denoising, patch-wise structural adaptive smoothing, total variation, nonlocal means, R. Description An account of the resource Image reconstruction from noisy data has a long history of methodological development and is based on a variety of ideas. In this paper we introduce a new method called patchwise adaptive smoothing, that extends the propagation-separation approach by using comparisons of local patches of image intensities to define local adaptive weighting schemes for an improved balance of reduced variability and bias in the reconstruction result. We present the implementation of the new method in an R package aws and demonstrate its properties on a number of examples in comparison with other state-of-the art image reconstruction methods Creator An entity primarily responsible for making the resource Jörg Polzehl Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i06 Publisher An entity responsible for making the resource available Weierstrass Institute Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Indonesia Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/b5d0e17abf30e0fa7c9586ecf4651170.pdf 4f931994a73be2cbe2baad8b708674ec 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 Computationally Efficient Simulation of Queues: The R Package queuecomputer Subject The topic of the resource queues, queueing theory, discrete event simulation, operations research, approximate Bayesian computation, R Description An account of the resource Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal. Creator An entity primarily responsible for making the resource Anthony Ebert Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i05 Publisher An entity responsible for making the resource available Queensland University of Technology ACEMS Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Inggris Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/d7c41c9c741f701d8ed68b151593fb75.pdf 04aa260a3591409cc3f245f919d68fc3 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 Zigzag Expanded Navigation Plots in R: The R Package zenplots Subject The topic of the resource : R, data visualization, data analysis, graphics, grid, loon. Description An account of the resource We describe the features and implementation of the R package zenplots (zigzag expanded navigation plots) for displaying high-dimensional data according to the recently proposed zenplots. By default, zenplots lay out alternating one- and two-dimensional plots in a zigzag-like pattern where adjacent axes share the same variate. Zenplots are especially useful when subsets of pairs can be identified as of particular interest by some measure, or as not meaningfully comparable, or when pairs of variates can be ordered in terms of potential interest to view, or the number of pairs is too large for more traditional layouts such as a scatterplot matrix. They also allow an essentially arbitrary layout of plots. A highdimensional space can be explored in a zenplot (zenplot()) by navigating through lower dimensional subspaces along a zenpath (zenpath()) which orders the dimensions (i.e., variates) visited according to some measure of interestingness; see Hofert and Oldford (2018) for an application to S&P 500 constituent data. The R package zenplots provides compact displays for high-dimensional data via the notion of zenplots, grouping of variates, and customizable displays of zigzag layouts. It accommodates different graphical systems including the base graphics package of R Core Team (2020b), the package grid of R Core Team (2020a) (and hence packages like ggplot2 of Wickham et al. 2020), and the interactive graphical package loon of Waddell and Oldford (2020). zenplots handles groups of variates, partial and fully missing data, and more. One important feature is that zenplot() and its auxiliary functions in zenplots distinguish layout from plotting which allows one to freely choose and create one- and twodimensional plot functions; predefined functions are exported for all graphical systems. All R plots in this paper are reproducible with the vignette "selected_features" (available in zenplots ≥ 0.0-2). Creator An entity primarily responsible for making the resource Marius Hofert Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i04 Publisher An entity responsible for making the resource available University of Waterloo Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Inggris Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/4d49f3d3e4fd55679527d54d2d4eb686.pdf 1febdfc95df22b51bd2227d93a070fb7 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 A Data Envelopment Analysis Toolbox for MATLAB Subject The topic of the resource data envelopment analysis, distance functions, technical efficiency, MATLAB Description An account of the resource The Data Envelopment Analysis Toolbox is a new package for MATLAB that includes functions to calculate the main data envelopment analysis models. The package includes code for the standard radial input, output and additive measures, allowing for constant and variable returns to scale, as well as recent developments related to the directional distance function, and including both desirable and undesirable outputs when measuring efficiency and productivity; i.e., Malmquist and Malmquist-Luenberger indices. Bootstrapping to perform statistical analysis is also included. This paper describes the methodology and implementation of the functions, and reports numerical results using a reliable productivity database on US agriculture to illustrate their use Creator An entity primarily responsible for making the resource José L. Zofío Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i03 Publisher An entity responsible for making the resource available Universidad Autónoma de Madrid Erasmus Research Institute of Management Erasmus University Rotterdam Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Inggris Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/c33ea0ae38d2e3dd2db8653b01c76788.pdf a6286675b6924e71e466d7901a055cec 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 Estimating Animal Abundance with N-Mixture Models Using the R-INLA Package for R Subject The topic of the resource : abundance, detection, JAGS, N-mixture model, R, R-INLA, unmarked, wildlife Description An account of the resource Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and, under appropriate conditions, produce abundance estimates that are less biased. Here, we demonstrate how to use the R-INLA package for R to analyze N-mixture models, and compare performance of R-INLA to two other common approaches: JAGS (via the runjags package for R), which uses Markov chain Monte Carlo and allows Bayesian inference, and the unmarked package for R, which uses maximum likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (i) fast computing times are necessary (R-INLA is 10 times faster than unmarked and 500 times faster than JAGS), (ii) familiar model syntax and data format (relative to other R packages) is desired, (iii) survey-level covariates of detection are not essential, and (iv) Bayesian inference is preferred Creator An entity primarily responsible for making the resource Timothy D. Meehan Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i02 Publisher An entity responsible for making the resource available National Audubon Society Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Inggris Type The nature or genre of the resource Text https://repository.horizon.ac.id/files/original/8aaaeea0927221c764cebad968170ef3.pdf 4136dd22a88ffae51afc4e52ad5a6bce 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 95 Tahun 2020 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v095 Date A point or period of time associated with an event in the lifecycle of the resource 2020 Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource Inggris 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 Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R Subject The topic of the resource : clustered data, covariance matrix estimator, object orientation, simulation, R. Description An account of the resource Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, and other social sciences. They are employed to adjust the inference following estimation of a standard least-squares regression or generalized linear model estimated by maximum likelihood. Although many publications just refer to “the” clustered standard errors, there is a surprisingly wide variety of clustered covariances, particularly due to different flavors of bias corrections. Furthermore, while the linear regression model is certainly the most important application case, the same strategies can be employed in more general models (e.g., for zero-inflated, censored, or limited responses). In R, functions for covariances in clustered or panel models have been somewhat scattered or available only for certain modeling functions, notably the (generalized) linear regression model. In contrast, an object-oriented approach to “robust” covariance matrix estimation – applicable beyond lm() and glm() – is available in the sandwich package but has been limited to the case of cross-section or time series data. Starting with sandwich 2.4.0, this shortcoming has been corrected: Based on methods for two generic functions (estfun() and bread()), clustered and panel covariances are provided in vcovCL(), vcovPL(), and vcovPC(). Moreover, clustered bootstrap covariances are provided in vcovBS(), using model update() on bootstrap samples. These are directly applicable to models from packages including MASS, pscl, countreg, and betareg, among many others. Some empirical illustrations are provided as well as an assessment of the methods’ performance in a simulation study Creator An entity primarily responsible for making the resource Achim Zeileis Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v095i01 Publisher An entity responsible for making the resource available Universität Innsbruck Date A point or period of time associated with an event in the lifecycle of the resource October 2020 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 Inggris Type The nature or genre of the resource Text