{"title":"robslopes: Efficient Computation of the (Repeated) Median Slope","authors":"Jakob Raymaekers, Ku Leuven","doi":"10.32614/rj-2023-012","DOIUrl":"https://doi.org/10.32614/rj-2023-012","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"89 1","pages":"38-49"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82525812","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}
G. Guernec, Valérie Garès, J. Omer, Philippe Saint-Pierre, N. Savy
The advances of information technologies often confront users with a large amount of data which is essential to integrate easily. In this context, creating a single database from multiple separate data sources can appear as an attractive but complex issue when same information of interest is stored in at least two distinct encodings. In this situation, merging the data sources consists in finding a common recoding scale to fill the incomplete information in a synthetic database. The OTrecod package provides R-users two functions dedicated to solve this recoding problem using optimal transportation theory. Specific arguments of these functions enrich the algorithms by relaxing distributional constraints or adding a regularization term to make the data fusion more flexible. The OTrecod package also provides a set of support functions dedicated to the harmonization of separate data sources, the handling of incomplete information and the selection of matching variables. This paper gives all the keys to quickly understand and master the original algorithms implemented in the OTrecod package, assisting step by step the user in its data fusion project.
{"title":"OTrecod: An R Package for Data Fusion using Optimal Transportation Theory","authors":"G. Guernec, Valérie Garès, J. Omer, Philippe Saint-Pierre, N. Savy","doi":"10.32614/rj-2023-006","DOIUrl":"https://doi.org/10.32614/rj-2023-006","url":null,"abstract":"The advances of information technologies often confront users with a large amount of data which is essential to integrate easily. In this context, creating a single database from multiple separate data sources can appear as an attractive but complex issue when same information of interest is stored in at least two distinct encodings. In this situation, merging the data sources consists in finding a common recoding scale to fill the incomplete information in a synthetic database. The OTrecod package provides R-users two functions dedicated to solve this recoding problem using optimal transportation theory. Specific arguments of these functions enrich the algorithms by relaxing distributional constraints or adding a regularization term to make the data fusion more flexible. The OTrecod package also provides a set of support functions dedicated to the harmonization of separate data sources, the handling of incomplete information and the selection of matching variables. This paper gives all the keys to quickly understand and master the original algorithms implemented in the OTrecod package, assisting step by step the user in its data fusion project.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"43 1","pages":"195-222"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83181551","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}
Miriam Esteve, Victor España, J. Aparicio, Xavier Barber
{"title":"eat: An R Package for fitting Efficiency Analysis Trees","authors":"Miriam Esteve, Victor España, J. Aparicio, Xavier Barber","doi":"10.32614/rj-2022-054","DOIUrl":"https://doi.org/10.32614/rj-2022-054","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"8 1","pages":"249-281"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74227025","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":"logitFD: an R package for functional principal component logit regression","authors":"M. Escabias, A. M. Aguilera, Christian Acal","doi":"10.32614/rj-2022-053","DOIUrl":"https://doi.org/10.32614/rj-2022-053","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"17 1","pages":"231-248"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81542997","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":"Will the Real Hopkins Statistic Please Stand Up?","authors":"Kevin Wright","doi":"10.32614/rj-2022-055","DOIUrl":"https://doi.org/10.32614/rj-2022-055","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"55 1","pages":"282-292"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81640815","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":"HDiR: An R Package for Computation and Nonparametric Plug-in Estimation of Directional Highest Density Regions and General Level Sets","authors":"P. Saavedra-Nieves, R. Crujeiras","doi":"10.32614/rj-2022-046","DOIUrl":"https://doi.org/10.32614/rj-2022-046","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"46 1","pages":"121-141"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89063626","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}
We describe the R package rbw, which implements the method of residual balancing weights (RBW) for estimating marginal structural models. In contrast to other methods such as inverse probability weighting (IPW) and covariate balancing propensity scores (CBPS), RBW involves modeling the conditional means of post-treatment confounders instead of the conditional distributions of the treatment to construct the weights. RBW is thus easier to use with continuous treatments, and the method is less susceptible to model misspecification issues that often arise when modeling the conditional distributions of treatments. RBW is also advantageous from a computational perspective. Because its weighting procedure involves a convex optimization problem, RBW typically locates a solution considerably faster than other methods whose optimization relies on nonconvex loss functions — such as the recently proposed nonparametric version of CBPS. We explain the rationale behind RBW, describe the functions in rbw, and then use real-world data to illustrate their applications in three scenarios: effect estimation for point treatments, causal mediation analysis, and effect estimation for time-varying treatments with time-varying confounders.
{"title":"rbw: An R Package for Constructing Residual Balancing Weights","authors":"Derick S. Baum, Xiang Zhou","doi":"10.32614/rj-2022-049","DOIUrl":"https://doi.org/10.32614/rj-2022-049","url":null,"abstract":"We describe the R package rbw, which implements the method of residual balancing weights (RBW) for estimating marginal structural models. In contrast to other methods such as inverse probability weighting (IPW) and covariate balancing propensity scores (CBPS), RBW involves modeling the conditional means of post-treatment confounders instead of the conditional distributions of the treatment to construct the weights. RBW is thus easier to use with continuous treatments, and the method is less susceptible to model misspecification issues that often arise when modeling the conditional distributions of treatments. RBW is also advantageous from a computational perspective. Because its weighting procedure involves a convex optimization problem, RBW typically locates a solution considerably faster than other methods whose optimization relies on nonconvex loss functions — such as the recently proposed nonparametric version of CBPS. We explain the rationale behind RBW, describe the functions in rbw, and then use real-world data to illustrate their applications in three scenarios: effect estimation for point treatments, causal mediation analysis, and effect estimation for time-varying treatments with time-varying confounders.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"112 1","pages":"174-192"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89344151","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}
Camille Frévent, M. Ahmed, J. Soula, L. Cucala, Zaineb Smida, S. Dabo‐Niang, M. Génin
{"title":"The R Package HDSpatialScan for the Detection of Clusters of Multivariate and Functional Data using Spatial Scan Statistics","authors":"Camille Frévent, M. Ahmed, J. Soula, L. Cucala, Zaineb Smida, S. Dabo‐Niang, M. Génin","doi":"10.32614/rj-2022-045","DOIUrl":"https://doi.org/10.32614/rj-2022-045","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"48 1","pages":"95-120"},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88041133","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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