Florian Pfisterer, Siyi Wei, Sebastian Vollmer, Michel Lang, Bernd Bischl
{"title":"Fairness Audits and Debiasing Using pkg{mlr3fairness}","authors":"Florian Pfisterer, Siyi Wei, Sebastian Vollmer, Michel Lang, Bernd Bischl","doi":"10.32614/rj-2023-034","DOIUrl":"https://doi.org/10.32614/rj-2023-034","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rolf Simoes, Alber Sanchez, Michelle C. A. Picoli, Patrick Meyfroidt
Segmentation methods are a valuable tool for exploring spatial data by identifying objects based on images' features. However, proper segmentation assessment is critical for obtaining high-quality results and running well-tuned segmentation algorithms Usually, various metrics are used to inform different types of errors that dominate the results. We describe a new R package, [segmetric](https://CRAN.R-project.org/package=segmetric), for assessing and analyzing the geospatial segmentation of satellite images. This package unifies code and knowledge spread across different software implementations and research papers to provide a variety of supervised segmentation metrics available in the literature. It also allows users to create their own metrics to evaluate the accuracy of segmented objects based on reference polygons. We hope this package helps to fulfill some of the needs of the R community that works with Earth Observation data.
{"title":"The segmetric Package: Metrics for Assessing Segmentation Accuracy for Geospatial Data","authors":"Rolf Simoes, Alber Sanchez, Michelle C. A. Picoli, Patrick Meyfroidt","doi":"10.32614/rj-2023-030","DOIUrl":"https://doi.org/10.32614/rj-2023-030","url":null,"abstract":"Segmentation methods are a valuable tool for exploring spatial data by identifying objects based on images' features. However, proper segmentation assessment is critical for obtaining high-quality results and running well-tuned segmentation algorithms Usually, various metrics are used to inform different types of errors that dominate the results. We describe a new R package, [segmetric](https://CRAN.R-project.org/package=segmetric), for assessing and analyzing the geospatial segmentation of satellite images. This package unifies code and knowledge spread across different software implementations and research papers to provide a variety of supervised segmentation metrics available in the literature. It also allows users to create their own metrics to evaluate the accuracy of segmented objects based on reference polygons. We hope this package helps to fulfill some of the needs of the R community that works with Earth Observation data.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Generalized partially linear single-index models (GPLSIMs) are important tools in nonparametric regression. They extend popular generalized linear models to allow flexible nonlinear dependence on some predictors while overcoming the "curse of dimensionality." We develop an R package gplsim that implements efficient spline estimation of GPLSIMs, proposed by [@yu_penalized_2002] and [@yu_penalised_2017], for a response variable from a general exponential family. The package builds upon the popular mgcv package for generalized additive models (GAMs) and provides functions that allow users to fit GPLSIMs with various link functions, select smoothing tuning parameter $lambda$ against generalized cross-validation or alternative choices, and visualize the estimated unknown univariate function of single-index term. In this paper, we discuss the implementation of gplsim in detail, and illustrate the use case through a sine-bump simulation study with various links and a real-data application to air pollution data.
{"title":"gplsim: An R Package for Generalized Partially Linear Single-index Models","authors":"Tianhai Zu, Yan Yu","doi":"10.32614/rj-2023-024","DOIUrl":"https://doi.org/10.32614/rj-2023-024","url":null,"abstract":"Generalized partially linear single-index models (GPLSIMs) are important tools in nonparametric regression. They extend popular generalized linear models to allow flexible nonlinear dependence on some predictors while overcoming the \"curse of dimensionality.\" We develop an R package gplsim that implements efficient spline estimation of GPLSIMs, proposed by [@yu_penalized_2002] and [@yu_penalised_2017], for a response variable from a general exponential family. The package builds upon the popular mgcv package for generalized additive models (GAMs) and provides functions that allow users to fit GPLSIMs with various link functions, select smoothing tuning parameter $lambda$ against generalized cross-validation or alternative choices, and visualize the estimated unknown univariate function of single-index term. In this paper, we discuss the implementation of gplsim in detail, and illustrate the use case through a sine-bump simulation study with various links and a real-data application to air pollution data.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The analysis of spatial and spatio-temporal point patterns is becoming increasingly necessary, given the rapid emergence of geographically and temporally indexed data in a wide range of fields. Non-parametric point pattern methods are a highly adaptable approach to answering questions about the real-world using complex data in the form of collections of points. Several methodological advances have been introduced in the last few years. This paper examines the current methodology, including the most recent developments in estimation and computation, and shows how various R packages can be combined to run a set of non-parametric point pattern analyses in a guided and intuitive way. An example of non-specific gastrointestinal disease reports in Hampshire, UK, from 2001 to 2003 is used to illustrate the methods, procedures and interpretations.
{"title":"Non-Parametric Analysis of Spatial and Spatio-Temporal Point Patterns","authors":"Jonatan A. González, Paula Moraga","doi":"10.32614/rj-2023-025","DOIUrl":"https://doi.org/10.32614/rj-2023-025","url":null,"abstract":"The analysis of spatial and spatio-temporal point patterns is becoming increasingly necessary, given the rapid emergence of geographically and temporally indexed data in a wide range of fields. Non-parametric point pattern methods are a highly adaptable approach to answering questions about the real-world using complex data in the form of collections of points. Several methodological advances have been introduced in the last few years. This paper examines the current methodology, including the most recent developments in estimation and computation, and shows how various R packages can be combined to run a set of non-parametric point pattern analyses in a guided and intuitive way. An example of non-specific gastrointestinal disease reports in Hampshire, UK, from 2001 to 2003 is used to illustrate the methods, procedures and interpretations.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The classical bootstrap has proven its usefulness in many areas of statistical inference. However, some shortcomings of this method are also known. Therefore, various bootstrap modifications and other resampling algorithms have been introduced, especially for real-valued data. Recently, bootstrap methods have become popular in statistical reasoning based on imprecise data often modeled by fuzzy numbers. One of the challenges faced there is to create bootstrap samples of fuzzy numbers which are similar to initial fuzzy samples but different in some way at the same time. These methods are implemented in [FuzzyResampling](https://CRAN.R-project.org/package=FuzzyResampling) package and applied in different statistical functions like single-sample or two-sample tests for the mean. Besides describing the aforementioned functions, some examples of their applications as well as numerical comparisons of the classical bootstrap with the new resampling algorithms are provided in this contribution.
{"title":"Resampling Fuzzy Numbers with Statistical Applications: FuzzyResampling Package","authors":"Maciej Romaniuk, Przemysław Grzegorzewski","doi":"10.32614/rj-2023-036","DOIUrl":"https://doi.org/10.32614/rj-2023-036","url":null,"abstract":"The classical bootstrap has proven its usefulness in many areas of statistical inference. However, some shortcomings of this method are also known. Therefore, various bootstrap modifications and other resampling algorithms have been introduced, especially for real-valued data. Recently, bootstrap methods have become popular in statistical reasoning based on imprecise data often modeled by fuzzy numbers. One of the challenges faced there is to create bootstrap samples of fuzzy numbers which are similar to initial fuzzy samples but different in some way at the same time. These methods are implemented in [FuzzyResampling](https://CRAN.R-project.org/package=FuzzyResampling) package and applied in different statistical functions like single-sample or two-sample tests for the mean. Besides describing the aforementioned functions, some examples of their applications as well as numerical comparisons of the classical bootstrap with the new resampling algorithms are provided in this contribution.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rafael Ayala, Daniel Ayala, Lara Sellés Vidal, David Ruiz
Over the past few years, the amount of artificial satellites orbiting Earth has grown fast, with close to a thousand new launches per year. Reliable calculation of the evolution of the satellites' position over time is required in order to efficiently plan the launch and operation of such satellites, as well as to avoid collisions that could lead to considerable losses and generation of harmful space debris. Here, we present asteRisk, the first R package for analysis of the trajectory of satellites. The package provides native implementations of different methods to calculate the orbit of satellites, as well as tools for importing standard file formats typically used to store satellite position data and to convert satellite coordinates between different frames of reference. Such functionalities provide the foundation for integrating orbital data and astrodynamics analysis in R.
{"title":"asteRisk - Integration and Analysis of Satellite Positional Data in R","authors":"Rafael Ayala, Daniel Ayala, Lara Sellés Vidal, David Ruiz","doi":"10.32614/rj-2023-023","DOIUrl":"https://doi.org/10.32614/rj-2023-023","url":null,"abstract":"Over the past few years, the amount of artificial satellites orbiting Earth has grown fast, with close to a thousand new launches per year. Reliable calculation of the evolution of the satellites' position over time is required in order to efficiently plan the launch and operation of such satellites, as well as to avoid collisions that could lead to considerable losses and generation of harmful space debris. Here, we present asteRisk, the first R package for analysis of the trajectory of satellites. The package provides native implementations of different methods to calculate the orbit of satellites, as well as tools for importing standard file formats typically used to store satellite position data and to convert satellite coordinates between different frames of reference. Such functionalities provide the foundation for integrating orbital data and astrodynamics analysis in R.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces an R package for ROC analysis in three-class classification problems, for clustered data in the presence of covariates, named ClusROC. The clustered data that we address have some hierarchical structure, i.e., dependent data deriving, for example, from longitudinal studies or repeated measurements. This package implements point and interval covariate-specific estimation of the true class fractions at a fixed pair of thresholds, the ROC surface, the volume under the ROC surface, and the optimal pairs of thresholds. We illustrate the usage of the implemented functions through two practical examples from different fields of research.
{"title":"ClusROC: An R Package for ROC Analysis in Three-Class Classification Problems for Clustered Data","authors":"Duc-Khanh To, Gianfranco Adimari, Monica Chiogna","doi":"10.32614/rj-2023-035","DOIUrl":"https://doi.org/10.32614/rj-2023-035","url":null,"abstract":"This paper introduces an R package for ROC analysis in three-class classification problems, for clustered data in the presence of covariates, named ClusROC. The clustered data that we address have some hierarchical structure, i.e., dependent data deriving, for example, from longitudinal studies or repeated measurements. This package implements point and interval covariate-specific estimation of the true class fractions at a fixed pair of thresholds, the ROC surface, the volume under the ROC surface, and the optimal pairs of thresholds. We illustrate the usage of the implemented functions through two practical examples from different fields of research.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many experiments can be modeled by a factorial design which allows statistical analysis of main factors and their interactions. A plethora of parametric inference procedures have been developed, for instance based on normality and additivity of the effects. However, often, it is not reasonable to assume a parametric model, or even normality, and effects may not be expressed well in terms of location shifts. In these situations, the use of a fully nonparametric model may be advisable. Nevertheless, until very recently, the straightforward application of nonparametric methods in complex designs has been hampered by the lack of a comprehensive R package. This gap has now been closed by the novel R-package [rankFD](https://CRAN.R-project.org/package=rankFD) that implements current state of the art nonparametric ranking methods for the analysis of factorial designs. In this paper, we describe its use, along with detailed interpretations of the results.
{"title":"rankFD: An R Software Package for Nonparametric Analysis of General Factorial Designs","authors":"Frank Konietschke, Edgar Brunner","doi":"10.32614/rj-2023-029","DOIUrl":"https://doi.org/10.32614/rj-2023-029","url":null,"abstract":"Many experiments can be modeled by a factorial design which allows statistical analysis of main factors and their interactions. A plethora of parametric inference procedures have been developed, for instance based on normality and additivity of the effects. However, often, it is not reasonable to assume a parametric model, or even normality, and effects may not be expressed well in terms of location shifts. In these situations, the use of a fully nonparametric model may be advisable. Nevertheless, until very recently, the straightforward application of nonparametric methods in complex designs has been hampered by the lack of a comprehensive R package. This gap has now been closed by the novel R-package [rankFD](https://CRAN.R-project.org/package=rankFD) that implements current state of the art nonparametric ranking methods for the analysis of factorial designs. In this paper, we describe its use, along with detailed interpretations of the results.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial distributions have been presented on alternative representations of geography, such as cartograms, for many years. In modern times, interactivity and animation have allowed alternative displays to play a larger role. Alternative representations have been popularised by online news sites, and digital atlases with a focus on public consumption. Applications are increasingly widespread, especially in the areas of disease mapping, and election results. The algorithm presented here creates a display that uses tessellated hexagons to represent a set of spatial polygons, and is implemented in the R package called sugarbag. It allocates these hexagons in a manner that preserves the spatial relationship of the geographic units, in light of their positions to points of interest. The display showcases spatial distributions, by emphasising the small geographical regions that are often difficult to locate on geographic maps.
{"title":"A Hexagon Tile Map Algorithm for Displaying Spatial Data","authors":"Stephanie Kobakian, Dianne Cook, Earl Duncan","doi":"10.32614/rj-2023-021","DOIUrl":"https://doi.org/10.32614/rj-2023-021","url":null,"abstract":"Spatial distributions have been presented on alternative representations of geography, such as cartograms, for many years. In modern times, interactivity and animation have allowed alternative displays to play a larger role. Alternative representations have been popularised by online news sites, and digital atlases with a focus on public consumption. Applications are increasingly widespread, especially in the areas of disease mapping, and election results. The algorithm presented here creates a display that uses tessellated hexagons to represent a set of spatial polygons, and is implemented in the R package called sugarbag. It allocates these hexagons in a manner that preserves the spatial relationship of the geographic units, in light of their positions to points of interest. The display showcases spatial distributions, by emphasising the small geographical regions that are often difficult to locate on geographic maps.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135236492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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