Scientific research is driven by our ability to use methods, procedures, and materials from previous studies and further research by adding to it. As the need for computationally-intensive methods to analyze large amounts of data grows, the criteria needed to achieve reproducibility, specifically computational reproducibility, have become more sophisticated. In general, prosaic descriptions of algorithms are not detailed or precise enough to ensure complete reproducibility of a method. Results may be sensitive to conditions not commonly specified in written-word descriptions such as implicit parameter settings or the programming language used. To achieve true computational reproducibility, it is necessary to provide all intermediate data and code used to produce published results. In this paper, we consider a class of algorithms developed to perform firearm evidence identification on cartridge case evidence known as the Congruent Matching Cells (CMC) methods. To date, these algorithms have been published as textual descriptions only. We introduce the first open-source implementation of the Congruent Matching Cells methods in the R package cmcR . We have structured the cmcR package as a set of sequential, modularized functions intended to ease the process of parameter experimentation. We use cmcR and a novel variance ratio statistic to explore the CMC methodology and demonstrate how to fill in the gaps when provided with computationally ambiguous descriptions of algorithms.
{"title":"A Study in Reproducibility: The Congruent Matching Cells Algorithm and cmcR Package","authors":"Joe Zemmels, Susan Vanderplas, H. Hofmann","doi":"10.32614/rj-2023-014","DOIUrl":"https://doi.org/10.32614/rj-2023-014","url":null,"abstract":"Scientific research is driven by our ability to use methods, procedures, and materials from previous studies and further research by adding to it. As the need for computationally-intensive methods to analyze large amounts of data grows, the criteria needed to achieve reproducibility, specifically computational reproducibility, have become more sophisticated. In general, prosaic descriptions of algorithms are not detailed or precise enough to ensure complete reproducibility of a method. Results may be sensitive to conditions not commonly specified in written-word descriptions such as implicit parameter settings or the programming language used. To achieve true computational reproducibility, it is necessary to provide all intermediate data and code used to produce published results. In this paper, we consider a class of algorithms developed to perform firearm evidence identification on cartridge case evidence known as the Congruent Matching Cells (CMC) methods. To date, these algorithms have been published as textual descriptions only. We introduce the first open-source implementation of the Congruent Matching Cells methods in the R package cmcR . We have structured the cmcR package as a set of sequential, modularized functions intended to ease the process of parameter experimentation. We use cmcR and a novel variance ratio statistic to explore the CMC methodology and demonstrate how to fill in the gaps when provided with computationally ambiguous descriptions of algorithms.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"7 1","pages":"79-102"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82558752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Population data provision is usually framed by regulations and restrictions and hence spatially aggregated in predefined enumeration units such as city blocks and census tracts. Many applications require population data at finer scale, and therefore, one may use downscaling methods to transform population counts from coarse spatial units into smaller ones. Although numerous methods for downscaling of population data have been reported in the scientific literature, only a limited number of implementation tools exist. In this study, we introduce populR, an R package that responds to this need. populR provides two downscaling methods, namely Areal Weighted Interpolation and Volume Weighted Interpolation, which are illustrated and compared to alternative implementations in the sf and areal packages using a case study from Mytilini, Greece. The results provide evidence that the vwi approach outperforms the others, and thus, we believe R users may gain significant advantage by using populR for population downscaling.
{"title":"populR: a Package for Population Downscaling in R","authors":"M. Batsaris, Dimitris Kavroudakis","doi":"10.32614/rj-2023-007","DOIUrl":"https://doi.org/10.32614/rj-2023-007","url":null,"abstract":"Population data provision is usually framed by regulations and restrictions and hence spatially aggregated in predefined enumeration units such as city blocks and census tracts. Many applications require population data at finer scale, and therefore, one may use downscaling methods to transform population counts from coarse spatial units into smaller ones. Although numerous methods for downscaling of population data have been reported in the scientific literature, only a limited number of implementation tools exist. In this study, we introduce populR, an R package that responds to this need. populR provides two downscaling methods, namely Areal Weighted Interpolation and Volume Weighted Interpolation, which are illustrated and compared to alternative implementations in the sf and areal packages using a case study from Mytilini, Greece. The results provide evidence that the vwi approach outperforms the others, and thus, we believe R users may gain significant advantage by using populR for population downscaling.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"3 1","pages":"223-234"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74373240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But there are few R packages focusing on evaluating the predictive power of survival models. This lack of handy software on evaluating survival predictions hinders further applications of survival analysis for practitioners. In this research, we want to fill this gap by providing an "all-in-one" R package which implements most predictive evaluation metrics in survival analysis. In the proposed SurvMetrics R package, we implement concordance index for both untied and tied survival data; we give a new calculation process of Brier score and integrated Brier score; we also extend the applicability of integrated absolute error and integrated square error for real data. For models that can output survival time predictions, a simplified metric called mean absolute error is also implemented. In addition, we test the effectiveness of all these metrics on simulated and real survival data sets. The newly developed SurvMetrics R package is available on CRAN at https://CRAN.R-project.org/package=SurvMetrics and GitHub at https://github.com/skyee1/SurvMetrics .
{"title":"SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis","authors":"Hanpu Zhou, Hong Wang, Sizheng Wang, Yi Zou","doi":"10.32614/rj-2023-009","DOIUrl":"https://doi.org/10.32614/rj-2023-009","url":null,"abstract":"Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But there are few R packages focusing on evaluating the predictive power of survival models. This lack of handy software on evaluating survival predictions hinders further applications of survival analysis for practitioners. In this research, we want to fill this gap by providing an \"all-in-one\" R package which implements most predictive evaluation metrics in survival analysis. In the proposed SurvMetrics R package, we implement concordance index for both untied and tied survival data; we give a new calculation process of Brier score and integrated Brier score; we also extend the applicability of integrated absolute error and integrated square error for real data. For models that can output survival time predictions, a simplified metric called mean absolute error is also implemented. In addition, we test the effectiveness of all these metrics on simulated and real survival data sets. The newly developed SurvMetrics R package is available on CRAN at https://CRAN.R-project.org/package=SurvMetrics and GitHub at https://github.com/skyee1/SurvMetrics .","PeriodicalId":20974,"journal":{"name":"R J.","volume":"2 1","pages":"252-263"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77597468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In biology a general definition for host switch is when an organism (consumer) uses a new host (which represents a resource). The host switch process by a consumer may happen through its pre-existing capability to use a sub-optimal resource. The HostSwitch R package provides functions to simulate the dynamics of host switching (extent and frequency) in the population of a consumer that interacts with current and potential hosts over the generations. The HostSwitch package is based on a Individual-Based mock-up model published in FORTRAN by Araujo et al. (2015). The package largely improve the previous mock-up model, by implementing numerous new functionalities such as comparison and evaluation of simulations with several customizable parameters to accommodate several types of biological consumer-host associations, an interactive visualization of the model, an in-depth description of the parameters in a biological context. Finally, we provided three real world scenarios taken from the literature selected from ecology, agriculture and parasitology. This package is intended to reach researchers in the broad field of biology interested in simulating the process of host switch of different types of symbiotic biological associations.
{"title":"HostSwitch: An R Package to Simulate the Extent of Host-Switching by a Consumer","authors":"V. Trivellone, Sabrina B. L. Araujo, B. Panassiti","doi":"10.32614/rj-2023-005","DOIUrl":"https://doi.org/10.32614/rj-2023-005","url":null,"abstract":"In biology a general definition for host switch is when an organism (consumer) uses a new host (which represents a resource). The host switch process by a consumer may happen through its pre-existing capability to use a sub-optimal resource. The HostSwitch R package provides functions to simulate the dynamics of host switching (extent and frequency) in the population of a consumer that interacts with current and potential hosts over the generations. The HostSwitch package is based on a Individual-Based mock-up model published in FORTRAN by Araujo et al. (2015). The package largely improve the previous mock-up model, by implementing numerous new functionalities such as comparison and evaluation of simulations with several customizable parameters to accommodate several types of biological consumer-host associations, an interactive visualization of the model, an in-depth description of the parameters in a biological context. Finally, we provided three real world scenarios taken from the literature selected from ecology, agriculture and parasitology. This package is intended to reach researchers in the broad field of biology interested in simulating the process of host switch of different types of symbiotic biological associations.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"38 1","pages":"179-194"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80322081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Generalized Mosaic Plots in the pkg{ggplot2} Framework","authors":"Haley Jeppson, H. Hofmann","doi":"10.32614/rj-2023-013","DOIUrl":"https://doi.org/10.32614/rj-2023-013","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"33 1","pages":"50-78"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72821968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Barbara Lerner, E. Boose, O. Brand, Aaron M. Ellison, E. Fong, Matthew K. Lau, K. Ngo, Thomas Pasquier, Luis A. Perez, M. Seltzer, Rose Sheehan, J. Wonsil
To be useful, scientific results must be reproducible and trustworthy. Data provenance—the history of data and how it was computed—underlies reproducibility of, and trust in, data analyses. Our work focuses on collecting data provenance from R scripts and providing tools that use the provenance to increase the reproducibility of and trust in analyses done in R. Specifically, our “End-to-end provenance tools” (“E2ETools”) use data provenance to: document the computing environment and inputs and outputs of a script’s execution; support script debugging and exploration; and explain differences in behavior across repeated executions of the same script. Use of these tools can help both the original author and later users of a script reproduce and trust its results.
{"title":"Making Provenance Work for You","authors":"Barbara Lerner, E. Boose, O. Brand, Aaron M. Ellison, E. Fong, Matthew K. Lau, K. Ngo, Thomas Pasquier, Luis A. Perez, M. Seltzer, Rose Sheehan, J. Wonsil","doi":"10.32614/rj-2023-003","DOIUrl":"https://doi.org/10.32614/rj-2023-003","url":null,"abstract":"To be useful, scientific results must be reproducible and trustworthy. Data provenance—the history of data and how it was computed—underlies reproducibility of, and trust in, data analyses. Our work focuses on collecting data provenance from R scripts and providing tools that use the provenance to increase the reproducibility of and trust in analyses done in R. Specifically, our “End-to-end provenance tools” (“E2ETools”) use data provenance to: document the computing environment and inputs and outputs of a script’s execution; support script debugging and exploration; and explain differences in behavior across repeated executions of the same script. Use of these tools can help both the original author and later users of a script reproduce and trust its results.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"7 1","pages":"141-159"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75721517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. J. Sáez-Castillo, A. Conde-Sánchez, Francisco Martínez
In recent years the use of regression models for under-dispersed count data, such as COM-Poisson or hyper-Poisson models, has increased. In this paper the DGLMExtPois package is presented. DGLMExtPois includes a new procedure to estimate the coefficients of a hyper-Poisson regression model within a GLM framework. The estimation process uses a gradient-based algorithm to solve a nonlinear constrained optimization problem. The package also provides an implementation of the COM-Poisson model, proposed by Huang (2017), to make it easy to compare both models. The functionality of the package is illustrated by fitting a model to a real dataset. Furthermore, an experimental comparison is made with other related packages, although none of these packages allow you to fit a hyper-Poisson model.
{"title":"DGLMExtPois: Advances in Dealing with Over and Under-dispersion in a Double GLM Framework","authors":"A. J. Sáez-Castillo, A. Conde-Sánchez, Francisco Martínez","doi":"10.32614/rj-2023-002","DOIUrl":"https://doi.org/10.32614/rj-2023-002","url":null,"abstract":"In recent years the use of regression models for under-dispersed count data, such as COM-Poisson or hyper-Poisson models, has increased. In this paper the DGLMExtPois package is presented. DGLMExtPois includes a new procedure to estimate the coefficients of a hyper-Poisson regression model within a GLM framework. The estimation process uses a gradient-based algorithm to solve a nonlinear constrained optimization problem. The package also provides an implementation of the COM-Poisson model, proposed by Huang (2017), to make it easy to compare both models. The functionality of the package is illustrated by fitting a model to a real dataset. Furthermore, an experimental comparison is made with other related packages, although none of these packages allow you to fit a hyper-Poisson model.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"85 1","pages":"121-140"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83632454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Songyan Yu, C. McBride, M. Frassl, M. Hipsey, David Hamilton
{"title":"dycdtools: an R Package for Assisting Calibration and Visualising Outputs of an Aquatic Ecosystem Model","authors":"Songyan Yu, C. McBride, M. Frassl, M. Hipsey, David Hamilton","doi":"10.32614/rj-2023-008","DOIUrl":"https://doi.org/10.32614/rj-2023-008","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"30 1","pages":"235-251"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87062350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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