Sylvia Harmening, Ann-Kristin Kreutzmann, Sören Schmidt, Nicola Salvati, Timo Schmid
The R package [emdi](https://CRAN.R-project.org/package=emdi) facilitates the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for model building, diagnostics, presenting, and exporting the results. The package version 1.1.7 includes unit-level small area models that rely on access to micro data. The area-level model by @Fay1979 and various extensions have been added to the package since the release of version 2.0.0. These extensions include (a) area-level models with back-transformations, (b) spatial and robust extensions, (c) adjusted variance estimation methods, and (d) area-level models that account for measurement errors. Corresponding mean squared error estimators are implemented for assessing the uncertainty. User-friendly tools like a stepwise variable selection, model diagnostics, benchmarking options, high quality maps and results exportation options enable a complete analysis procedure. The functionality of the package is illustrated by examples based on synthetic data for Austrian districts.
{"title":"A Framework for Producing Small Area Estimates Based on Area-Level Models in R","authors":"Sylvia Harmening, Ann-Kristin Kreutzmann, Sören Schmidt, Nicola Salvati, Timo Schmid","doi":"10.32614/rj-2023-039","DOIUrl":"https://doi.org/10.32614/rj-2023-039","url":null,"abstract":"The R package [emdi](https://CRAN.R-project.org/package=emdi) facilitates the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for model building, diagnostics, presenting, and exporting the results. The package version 1.1.7 includes unit-level small area models that rely on access to micro data. The area-level model by @Fay1979 and various extensions have been added to the package since the release of version 2.0.0. These extensions include (a) area-level models with back-transformations, (b) spatial and robust extensions, (c) adjusted variance estimation methods, and (d) area-level models that account for measurement errors. Corresponding mean squared error estimators are implemented for assessing the uncertainty. User-friendly tools like a stepwise variable selection, model diagnostics, benchmarking options, high quality maps and results exportation options enable a complete analysis procedure. The functionality of the package is illustrated by examples based on synthetic data for Austrian districts.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135926827","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}
Multi-state models can be used to describe processes in which an individual moves through a finite number of states in continuous time. These models allow a detailed view of the evolution or recovery of the process and can be used to study the effect of a vector of explanatory variables on the transition intensities or to obtain prediction probabilities of future events after a given event history. In both cases, before using these models, we have to evaluate whether the Markov assumption is tenable. This paper introduces the [markovMSM](https://CRAN.R-project.org/package=markovMSM) package, a software application for R, which considers tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markovian Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where individuals are grouped by the state occupied by the process at a particular time point. The main functionalities of the [markovMSM](https://CRAN.R-project.org/package=markovMSM) package are illustrated using real data examples.
{"title":"markovMSM: An R Package for Checking the Markov Condition in Multi-State Survival Data","authors":"Gustavo Soutinho, Luís Meira-Machado","doi":"10.32614/rj-2023-032","DOIUrl":"https://doi.org/10.32614/rj-2023-032","url":null,"abstract":"Multi-state models can be used to describe processes in which an individual moves through a finite number of states in continuous time. These models allow a detailed view of the evolution or recovery of the process and can be used to study the effect of a vector of explanatory variables on the transition intensities or to obtain prediction probabilities of future events after a given event history. In both cases, before using these models, we have to evaluate whether the Markov assumption is tenable. This paper introduces the [markovMSM](https://CRAN.R-project.org/package=markovMSM) package, a software application for R, which considers tests of the Markov assumption that are applicable to general multi-state models. Three approaches using existing methodology are considered: a simple method based on including covariates depending on the history; methods based on measuring the discrepancy of the non-Markov estimators of the transition probabilities to the Markovian Aalen-Johansen estimators; and, finally, methods that were developed by considering summaries from families of log-rank statistics where individuals are grouped by the state occupied by the process at a particular time point. The main functionalities of the [markovMSM](https://CRAN.R-project.org/package=markovMSM) package are illustrated using real data examples.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135926828","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}
Bacher, Peder, Bergsteinsson, Hjörleifur G., Frölke, Linde, Sørensen, Mikkel L., Lemos-Vinasco, Julian, Liisberg, Jon, Møller, Jan Kloppenborg, Nielsen, Henrik Aalborg, Madsen, Henrik
Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the [R]{.sans-serif} package [[onlineforecast](https://onlineforecasting.org)]{.sans-serif} that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.
{"title":"Onlineforecast: An R Package for Adaptive and Recursive Forecasting","authors":"Bacher, Peder, Bergsteinsson, Hjörleifur G., Frölke, Linde, Sørensen, Mikkel L., Lemos-Vinasco, Julian, Liisberg, Jon, Møller, Jan Kloppenborg, Nielsen, Henrik Aalborg, Madsen, Henrik","doi":"10.32614/rj-2023-031","DOIUrl":"https://doi.org/10.32614/rj-2023-031","url":null,"abstract":"Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, require frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the [R]{.sans-serif} package [[onlineforecast](https://onlineforecasting.org)]{.sans-serif} that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of forecasts as model inputs, e.g. numerical weather forecast. Users can create new models for their particular applications and run models in an operational setting. The package also allows users to easily replace parts of the setup, e.g. using new methods for estimation. The package comes with comprehensive vignettes and examples of online forecasting applications in energy systems, but can easily be applied for online forecasting in all fields.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136364837","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}
{"title":"Robust Functional Linear Regression Models","authors":"Ufuk Beyaztas, Han Lin Shang","doi":"10.32614/rj-2023-033","DOIUrl":"https://doi.org/10.32614/rj-2023-033","url":null,"abstract":"","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135099007","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}
Saptarshi Chakraborty, Marianthi Markatou, Robert Ball
Medical product safety continues to be a key concern of the twenty-first century. Several spontaneous adverse events reporting databases established across the world continuously collect and archive adverse events data on various medical products. Determining signals of disproportional reporting (SDR) of product/adverse event pairs from these large-scale databases require the use of principled statistical techniques. Likelihood ratio test (LRT)-based approaches are particularly noteworthy in this context as they permit objective SDR detection without requiring ad hoc thresholds. However, their implementation is non-trivial due to analytical complexities, which necessitate the use of computation-heavy methods. Here we introduce R package pvLRT which implements a suite of LRT approaches, along with various post-processing and graphical summary functions, to facilitate simplified use of the methodologies. Detailed examples are provided to illustrate the package through analyses of three real product safety datasets obtained from publicly available FDA FAERS and VAERS databases.
{"title":"Likelihood Ratio Test-Based Drug Safety Assessment using R Package pkg{pvLRT}","authors":"Saptarshi Chakraborty, Marianthi Markatou, Robert Ball","doi":"10.32614/rj-2023-027","DOIUrl":"https://doi.org/10.32614/rj-2023-027","url":null,"abstract":"Medical product safety continues to be a key concern of the twenty-first century. Several spontaneous adverse events reporting databases established across the world continuously collect and archive adverse events data on various medical products. Determining signals of disproportional reporting (SDR) of product/adverse event pairs from these large-scale databases require the use of principled statistical techniques. Likelihood ratio test (LRT)-based approaches are particularly noteworthy in this context as they permit objective SDR detection without requiring ad hoc thresholds. However, their implementation is non-trivial due to analytical complexities, which necessitate the use of computation-heavy methods. Here we introduce R package pvLRT which implements a suite of LRT approaches, along with various post-processing and graphical summary functions, to facilitate simplified use of the methodologies. Detailed examples are provided to illustrate the package through analyses of three real product safety datasets obtained from publicly available FDA FAERS and VAERS databases.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"55 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":"135236489","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}
Several R packages have been developed to study cross-phenotypes associations (or pleiotropy) at the SNP-level, based on summary statistics data from genome-wide association studies (GWAS). However, none of them allow for consideration of the underlying group structure of the data. We developed an R package, entitled GCPBayes (Group level Bayesian Meta-Analysis for Studying Cross-Phenotype Genetic Associations), introduced by Baghfalaki et al. (2021), that implements continuous and Dirac spike priors for group selection, and also a Bayesian sparse group selection approach with hierarchical spike and slab priors, to select important variables at the group level and within the groups. The methods use summary statistics data from association studies or individual level data as inputs, and perform Bayesian meta-analysis approaches across multiple phenotypes to detect pleiotropy at both group-level (e.g., at the gene or pathway level) and within group (e.g., at the SNP level).
{"title":"GCPBayes: An R package for studying Cross-Phenotype Genetic Associations with Group-level Bayesian Meta-Analysis","authors":"Taban Baghfalaki, Pierre-Emmanuel Sugier, Yazdan Asgari, Thérèse Truong, Benoit Liquet","doi":"10.32614/rj-2023-028","DOIUrl":"https://doi.org/10.32614/rj-2023-028","url":null,"abstract":"Several R packages have been developed to study cross-phenotypes associations (or pleiotropy) at the SNP-level, based on summary statistics data from genome-wide association studies (GWAS). However, none of them allow for consideration of the underlying group structure of the data. We developed an R package, entitled GCPBayes (Group level Bayesian Meta-Analysis for Studying Cross-Phenotype Genetic Associations), introduced by Baghfalaki et al. (2021), that implements continuous and Dirac spike priors for group selection, and also a Bayesian sparse group selection approach with hierarchical spike and slab priors, to select important variables at the group level and within the groups. The methods use summary statistics data from association studies or individual level data as inputs, and perform Bayesian meta-analysis approaches across multiple phenotypes to detect pleiotropy at both group-level (e.g., at the gene or pathway level) and within group (e.g., at the SNP level).","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"45 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":"135236487","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}
Several new tests have been proposed for testing interaction in unreplicated two-way analysis of variance models. Unfortunately, each test is powerful for detecting a pattern of interaction. Therefore, it is reasonable to combine multiple interaction tests to increase the power of detection for significant interactions. We introduce the package [combinIT](https://CRAN.R-project.org/package=combinIT) that provides researchers the results of six existing recommended interaction tests, including: the value of test statistics, exact Monte Carlo p-values, approximated or adjusted p-values, the results of four combined tests and explanations of interaction types if the discussed tests are significant. The software combinIT is a more comprehensive R package in comparison with the two existing packages. In addition, the software is executed quickly to obtain the exact Monte Carlo p-values, even for large Monte Carlo runs, in contrast to existing packages.
{"title":"combinIT: An R Package for Combining Interaction Tests for Unreplicated Two-Way Tables","authors":"Mahmood Kharrati-Kopaei, Zahra Shenavari, Hossein Haghbin","doi":"10.32614/rj-2023-037","DOIUrl":"https://doi.org/10.32614/rj-2023-037","url":null,"abstract":"Several new tests have been proposed for testing interaction in unreplicated two-way analysis of variance models. Unfortunately, each test is powerful for detecting a pattern of interaction. Therefore, it is reasonable to combine multiple interaction tests to increase the power of detection for significant interactions. We introduce the package [combinIT](https://CRAN.R-project.org/package=combinIT) that provides researchers the results of six existing recommended interaction tests, including: the value of test statistics, exact Monte Carlo p-values, approximated or adjusted p-values, the results of four combined tests and explanations of interaction types if the discussed tests are significant. The software combinIT is a more comprehensive R package in comparison with the two existing packages. In addition, the software is executed quickly to obtain the exact Monte Carlo p-values, even for large Monte Carlo runs, in contrast to existing packages.","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":"135236479","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}
Jincheng Zhou, Jinhui Yang, James S. Hodges, Lifeng Lin, Haitao Chu
Noncompliance, a common problem in randomized clinical trials (RCTs), complicates the analysis of the causal treatment effect, especially in meta-analysis of RCTs. The complier average causal effect (CACE) measures the effect of an intervention in the latent subgroup of the population that complies with its assigned treatment (the compliers). Recently, Bayesian hierarchical approaches have been proposed to estimate the CACE in a single RCT and a meta-analysis of RCTs. We develop an R package, BayesCACE, to provide user-friendly functions for implementing CACE analysis for binary outcomes based on the flexible Bayesian hierarchical framework. This package includes functions for analyzing data from a single study and for performing a meta-analysis with either complete or incomplete compliance data. The package also provides various functions for generating forest, trace, posterior density, and auto-correlation plots, which can be useful to review noncompliance rates, visually assess the model, and obtain study-specific and overall CACEs.
{"title":"Estimating Causal Effects using Bayesian Methods with the R Package BayesCACE","authors":"Jincheng Zhou, Jinhui Yang, James S. Hodges, Lifeng Lin, Haitao Chu","doi":"10.32614/rj-2023-038","DOIUrl":"https://doi.org/10.32614/rj-2023-038","url":null,"abstract":"Noncompliance, a common problem in randomized clinical trials (RCTs), complicates the analysis of the causal treatment effect, especially in meta-analysis of RCTs. The complier average causal effect (CACE) measures the effect of an intervention in the latent subgroup of the population that complies with its assigned treatment (the compliers). Recently, Bayesian hierarchical approaches have been proposed to estimate the CACE in a single RCT and a meta-analysis of RCTs. We develop an R package, BayesCACE, to provide user-friendly functions for implementing CACE analysis for binary outcomes based on the flexible Bayesian hierarchical framework. This package includes functions for analyzing data from a single study and for performing a meta-analysis with either complete or incomplete compliance data. The package also provides various functions for generating forest, trace, posterior density, and auto-correlation plots, which can be useful to review noncompliance rates, visually assess the model, and obtain study-specific and overall CACEs.","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":"135236480","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 proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package.
{"title":"A Clustering Algorithm to Organize Satellite Hotspot Data for the Purpose of Tracking Bushfires Remotely","authors":"Weihao Li, Emily Dodwell, Dianne Cook","doi":"10.32614/rj-2023-022","DOIUrl":"https://doi.org/10.32614/rj-2023-022","url":null,"abstract":"This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"45 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":"135236483","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}
A nonlinear mixed effects model is useful when the data are repeatedly measured within the same unit or correlated between units. Such models are widely used in medicine, disease mechanics, pharmacology, ecology, social science, psychology, etc. After fitting the nonlinear mixed effect model, model diagnostics are essential for verifying that the results are reliable. The visual predictive check (VPC) has recently been highlighted as a visual diagnostic tool for pharmacometric models. This method can also be applied to general nonlinear mixed effects models. However, functions for VPCs in existing R packages are specialized for pharmacometric model diagnosis, and are not suitable for general nonlinear mixed effect models. In this paper, we propose nlmeVPC, an R package for the visual diagnosis of various nonlinear mixed effect models. The nlmeVPC package allows for more diverse model diagnostics, including visual diagnostic tools that extend the concept of VPCs along with the capabilities of existing R packages.
{"title":"nlmeVPC: Visual Model Diagnosis for the Nonlinear Mixed Effect Model","authors":"Eun-Hwa Kang, Myungji Ko, Eun-Kyung Lee","doi":"10.32614/rj-2023-026","DOIUrl":"https://doi.org/10.32614/rj-2023-026","url":null,"abstract":"A nonlinear mixed effects model is useful when the data are repeatedly measured within the same unit or correlated between units. Such models are widely used in medicine, disease mechanics, pharmacology, ecology, social science, psychology, etc. After fitting the nonlinear mixed effect model, model diagnostics are essential for verifying that the results are reliable. The visual predictive check (VPC) has recently been highlighted as a visual diagnostic tool for pharmacometric models. This method can also be applied to general nonlinear mixed effects models. However, functions for VPCs in existing R packages are specialized for pharmacometric model diagnosis, and are not suitable for general nonlinear mixed effect models. In this paper, we propose nlmeVPC, an R package for the visual diagnosis of various nonlinear mixed effect models. The nlmeVPC package allows for more diverse model diagnostics, including visual diagnostic tools that extend the concept of VPCs along with the capabilities of existing R packages.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"40 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":"135236484","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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