1 10 10 https://repository.horizon.ac.id/files/original/40e2c679c27e73a4531dcf0c9ef2b3ff.pdf 019aa1394f60ddf41711ce7395cfe842 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 clinicalsignificance: Clinical Significance Analyses of Intervention Studies in R Subject The topic of the resource clinical significance, psychotherapy, clinical studies, intervention studies, treatment effect, intervention effect, R Description An account of the resource The analysis of clinical significance is helpful to decide if an intervention leads to practically relevant or meaningful changes for individual patients which is clearly different from the analysis of statistical significance. However, the framework is used rarely and inconsistently. We introduce the R package clinicalsignificance to harness the use of clinical significance analysis of intervention trials in clinical research. This package provides all relevant methods to calculate and present analyses of clinical significance in a consistent form and easy to use implementation. Despite its shortcomings, clinical significance analyses are a valuable tool to gain more insight into intended and potential unintended intervention effects and they may improve the interpretation and comparability of intervention trial results. Lastly, analyses of clinical significance may guide researchers and policy makers in determining which interventions are clinically effective. Creator An entity primarily responsible for making the resource Benedikt B. Claus Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i01 Publisher An entity responsible for making the resource available Witten/Herdecke University Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/ecddb4dc059d5576185f2f27e60adb34.pdf 884d886a9906e151dc366b26fa2e7e31 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 Estimating Conditional Distributions with Neural Networks Using R Package deeptrafo Subject The topic of the resource : deep learning, distributional regression, neural networks, transformation models Description An account of the resource Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classical regression models either break down under the computational load of processing such data or require additional manual feature extraction to make these problems tractable. Here, we present deeptrafo, a package for fitting flexible regression models for conditional distributions using a tensorflow back end with numerous additional processors, such as neural networks, penalties, and smoothing splines. Package deeptrafo implements deep conditional transformation models (DCTMs) for binary, ordinal, count, survival, continuous, and time series responses, potentially with uninformative censoring. Unlike other available methods, DCTMs do not assume a parametric family of distributions for the response. Further, the data analyst may trade off interpretability and flexibility by supplying custom neural network architectures and smoothers for each term in an intuitive formula interface. We demonstrate how to set up, fit, and work with DCTMs for several response types. We further showcase how to construct ensembles of these models, evaluate models using inbuilt cross-validation, and use other convenience functions for DCTMs in several applications. Lastly, we discuss DCTMs in light of other approaches to regression with non-tabular data Creator An entity primarily responsible for making the resource Lucas Kook Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i10 Publisher An entity responsible for making the resource available Vienna University of Economics and Business Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/13885c0ddc75541a43f8740f1d006210.pdf 26559955340a77325480c5c9525d6c10 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 jti and sparta: Time and Space Efficient Packages for Model-Based Prediction in Large Bayesian Networks Subject The topic of the resource Bayesian networks, junction trees, sparse tables, R, C++. Description An account of the resource A Bayesian network is a multivariate (potentially very high dimensional) probabilistic model formed by combining lower-dimensional components. In Bayesian networks, the computation of conditional probabilities is fundamental for model-based predictions. This is usually done based on message passing algorithms that utilize conditional independence structures. In this paper, we deal with a specific message passing algorithm that exploits a second structure called a junction tree and hence is known as the junction tree algorithm (JTA). In Bayesian networks for discrete variables with finite state spaces, there is a fundamental problem in high dimensions: A discrete distribution is represented by a table of values, and in high dimensions, such tables can become prohibitively large. In JTA, such tables must be multiplied which can lead to even larger tables. The jti package meets this challenge by using the package sparta by implementing methods that efficiently handle multiplication and marginalization of sparse tables through JTA. The two packages are written in the R programming language and are freely available from the Comprehensive R Archive Network. Creator An entity primarily responsible for making the resource Mads Lindskou Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i02 Publisher An entity responsible for making the resource available Aalborg University Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/64b99668aa6365abcf9c6db5601466bc.pdf aa100519bd8d53f0e13c5882da7b5b97 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 GET: Global Envelopes in R Subject The topic of the resource functional linear model, central region, goodness-of-fit, graphical normality test, Monte Carlo test, multiple testing, permutation test, R, spatial point pattern. Description An account of the resource This work describes the R package GET that implements global envelopes for a general set of d-dimensional vectors T in various applications. A 100(1−α)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to α. The term ‘global’ means that this probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be employed for central regions of functional or multivariate data, for graphical Monte Carlo and permutation tests where the test statistic is multivariate or functional, and for global confidence and prediction bands. Intrinsic graphical interpretation property is introduced for global envelopes. The global envelopes included in the GET package have this property, which particularly helps to interpret test results, by providing a graphical interpretation that shows the reasons of rejection of the tested hypothesis. Examples of different uses of global envelopes and their implementation in the GET package are presented, including global envelopes for single and several one- or two-dimensional functions, Monte Carlo goodness-of-fit tests for simple and composite hypotheses, comparison of distributions, functional analysis of variance, functional linear model, and confidence bands in polynomial regression Creator An entity primarily responsible for making the resource Mari Myllymäki Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i03 Publisher An entity responsible for making the resource available Natural Resources Institute Finland (Luke) Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/a9346d1dad6261a47da75f8d09daf493.pdf 89aa179b8c65db9f3b84806a4318f091 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series Subject The topic of the resource BEKK model, multivariate GARCH, leverage effect, value-at-risk, impulse response functions, R. Description An account of the resource We describe the R package BEKKs, which implements the estimation and diagnostic analysis of a prominent family of multivariate generalized autoregressive conditionally heteroskedastic (MGARCH) processes, the so-called BEKK models. Unlike existing software packages, we make use of analytical derivatives implemented in efficient C++ code for nonlinear log-likelihood optimization. This allows fast parameter estimation even in higher model dimensions N > 3. The baseline BEKK model is complemented with an asymmetric parameterization that allows for a flexible modeling of conditional (co)variances. Furthermore, we provide the user with the simplified scalar and diagonal BEKK models to deal with high dimensionality of heteroskedastic time series. The package is designed in an object-oriented way featuring a comprehensive toolbox of methods to investigate and interpret, for instance, volatility impulse response functions, risk estimation and forecasting (VaR) and a backtesting algorithm to compare the forecasting performance of alternative BEKK models. For illustrative purposes, we analyze a bivariate ETF return series (S&P, US treasury bonds) and a four-dimensional system comprising, in addition, a gold ETF and changes of a log oil price by means of the suggested package. We find that the BEKKs package is more than 100 times faster for time series systems of dimension N > 3 than other existing packages. Creator An entity primarily responsible for making the resource Markus J. Fülle Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i04 Publisher An entity responsible for making the resource available University of Göttingen Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/94df6bf998482a352f7b862445baaf67.pdf a0ababd49dd6d084a3711b38a4c380a4 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 Interpreting Deep Neural Networks with the Package innsight Subject The topic of the resource neural networks, feature attribution, interpretable machine learning, explainable artificial intelligence, XAI, IML, torch, keras, R. Description An account of the resource The R package innsight offers a general toolbox for revealing variable-wise interpretations of deep neural networks’ predictions with so-called feature attribution methods. Aside from the unified and user-friendly framework, the package stands out in three ways: It is generally the first R package implementing feature attribution methods for neural networks. Secondly, it operates independently of the deep learning library, allowing the interpretation of neural networks from any R package, including keras, torch, neuralnet, and even custom models. Despite its flexibility, innsight benefits internally from the torch package’s fast and efficient array calculations, which builds on LibTorch – PyTorch’s C++ backend – without a Python dependency. Finally, it offers a variety of visualization tools for tabular, signal, image data, or a combination of these. Additionally, the plots can be rendered interactively using the plotly package. Creator An entity primarily responsible for making the resource Niklas Koenen Source A related resource from which the described resource is derived Leibniz Institute for Prevention Research and Epidemiology – BIPS, University of Bremen Publisher An entity responsible for making the resource available Leibniz Institute for Prevention Research and Epidemiology – BIPS, University of Bremen Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/3e672ba58dee6d7e6086babd14c2201e.pdf ef7f55bdd2abebb7ff5dafeaf79399f0 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 mlr3spatiotempcv: Spatiotemporal Resampling Methods for Machine Learning in R Subject The topic of the resource cross-validation, predictive performance, machine learning, autocorrelation, spatial, temporal, R. Description An account of the resource Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution provides an overview of the state-of-the-art in spatial and spatiotemporal cross-validation techniques and their implementations in R while introducing the R package mlr3spatiotempcv as an extension package of the machine-learning framework mlr3. Currently various R packages implementing different spatiotemporal partitioning strategies exist: blockCV, CAST, skmeans and sperrorest. The goal of mlr3spatiotempcv is to gather the available spatiotemporal resampling methods in R and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the mlr3 machinelearning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied. Creator An entity primarily responsible for making the resource Patrick Schratz Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i07 Publisher An entity responsible for making the resource available Friedrich Schiller University Jena Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/50b7c339cb7504c9e5a8c79cf89837e2.pdf 0aaededaaad0f20a10e6881a07430911 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 pyStoNED: A Python Package for Convex Regression and Frontier Estimation Subject The topic of the resource multivariate convex regression, nonparametric least squares, frontier estimation, efficiency analysis, stochastic noise, Python. Description An account of the resource Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning, and related fields. In the field of productivity and efficiency analysis, recent developments in multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression have bridged the long-standing gap between the conventional deterministic-nonparametric and stochastic-parametric methods. Unfortunately, the heavy computational burden and the lack of a powerful, reliable, and fully open-access computational package have slowed down the diffusion of these advanced estimation techniques to the empirical practice. The purpose of the Python package pyStoNED is to address this challenge by providing a freely available and user-friendly tool for multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. This paper presents a tutorial of the pyStoNED package and illustrates its application, focusing on estimating frontier cost and production functions Creator An entity primarily responsible for making the resource Sheng Dai Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i06 Publisher An entity responsible for making the resource available Zhongnan University of Economics and Law Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/78958809424735da257df46098540f6f.pdf 274d96952f896723bd95b36dc97603c5 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 Birth-and-Death Processes in Python: The BirDePy Package Subject The topic of the resource ABC algorithm, birth-and-death processes, continuous-time Markov chains, diffusion approximation, EM algorithm, Erlangization, Laplace transform, maximum likelihood estimation, parameter estimation, Python, uniformization. Description An account of the resource Birth-and-death processes (BDPs) form a class of continuous-time Markov chains that are particularly suited to describing the changes in the size of a population over time. Population-size-dependent BDPs (PSDBDPs) allow the rate at which a population grows to depend on the current population size. The main purpose of our new Python package BirDePy is to provide easy-to-use functions that allow the parameters of discretelyobserved PSDBDPs to be estimated. The package can also be used to estimate parameters of continuously-observed PSDBDPs, simulate sample paths, approximate transition probabilities, and generate forecasts. We describe in detail several methods which have been incorporated into BirDePy to achieve each of these tasks. The usage and effectiveness of the package is demonstrated through a variety of examples of PSDBDPs, as well as case studies involving annual population count data of two endangered bird species. Creator An entity primarily responsible for making the resource Sophie Hautphenne Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i05 Publisher An entity responsible for making the resource available The University of Melbourne Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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/f58a4c193ffd2ca8003749d1de69452d.pdf 9edfc0dcf3fc602ac6eb257e62079840 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 111 Tahun 2024 Source A related resource from which the described resource is derived https://www.jstatsoft.org/issue/view/v111 Date A point or period of time associated with an event in the lifecycle of the resource 2024 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 How to Interpret Statistical Models Using marginaleffects for R and Python Subject The topic of the resource marginal effect, marginal mean, slope, prediction, fitted value, contrast, comparison, R, Python. Description An account of the resource The parameters of a statistical model can sometimes be difficult to interpret substantively, especially when that model includes nonlinear components, interactions, or transformations. Analysts who fit such complex models often seek to transform raw parameter estimates into quantities that are easier for domain experts and stakeholders to understand. This article presents a simple conceptual framework to describe a vast array of such quantities of interest, which are reported under imprecise and inconsistent terminology across disciplines: predictions, marginal predictions, marginal means, marginal effects, conditional effects, slopes, contrasts, risk ratios, etc. We introduce marginaleffects, a package for R and Python which offers a simple and powerful interface to compute all of those quantities, and to conduct (non-)linear hypothesis and equivalence tests on them. marginaleffects is lightweight; extensible; it works well in combination with other R and Python packages; and it supports over 100 classes of models, including linear, generalized linear, generalized additive, mixed effects, Bayesian, and several machine learning models. Creator An entity primarily responsible for making the resource Vincent Arel-Bundock Source A related resource from which the described resource is derived https://www.jstatsoft.org/article/view/v111i09 Publisher An entity responsible for making the resource available Université de Montréal Date A point or period of time associated with an event in the lifecycle of the resource November 2024 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