R JournalPub Date : 2022-09-01DOI: 10.32614/rj-2022-043
Ehsan Masoudi, Heinz Holling, Weng Kee Wong, Seongho Kim
{"title":"ICAOD: An R Package for Finding Optimal designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm.","authors":"Ehsan Masoudi, Heinz Holling, Weng Kee Wong, Seongho Kim","doi":"10.32614/rj-2022-043","DOIUrl":"https://doi.org/10.32614/rj-2022-043","url":null,"abstract":"<p><p>Optimal design ideas are increasingly used in different disciplines to rein in experimental costs. Given a nonlinear statistical model and a design criterion, optimal designs determine the number of experimental points to observe the responses, the design points and the number of replications at each design point. Currently, there are very few free and effective computing tools for finding different types of optimal designs for a general nonlinear model, especially when the criterion is not differentiable. We introduce an R package ICAOD to find various types of optimal designs and they include locally, minimax and Bayesian optimal designs for different nonlinear statistical models. Our main computational tool is a novel metaheuristic algorithm called imperialist competitive algorithm (ICA) and inspired by socio-political behavior of humans and colonialism. We demonstrate its capability and effectiveness using several applications. The package also includes several theory-based tools to assess optimality of a generated design when the criterion is a convex function of the design.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"14 3","pages":"20-45"},"PeriodicalIF":2.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912186/pdf/nihms-1865401.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10708621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R JournalPub Date : 2022-09-01Epub Date: 2022-12-19DOI: 10.32614/rj-2022-047
Daeyoung Lim, Ming-Hui Chen, Joseph G Ibrahim, Sungduk Kim, Arvind K Shah, Jianxin Lin
{"title":"metapack: An R Package for Bayesian Meta-Analysis and Network Meta-Analysis with a Unified Formula Interface.","authors":"Daeyoung Lim, Ming-Hui Chen, Joseph G Ibrahim, Sungduk Kim, Arvind K Shah, Jianxin Lin","doi":"10.32614/rj-2022-047","DOIUrl":"10.32614/rj-2022-047","url":null,"abstract":"<p><p>Meta-analysis, a statistical procedure that compares, combines, and synthesizes research findings from multiple studies in a principled manner, has become popular in a variety of fields. Meta-analyses using study-level (or equivalently <i>aggregate</i>) data are of particular interest due to data availability and modeling flexibility. In this paper, we describe an R package <b>metapack</b> that introduces a unified formula interface for both meta-analysis and network meta-analysis. The user interface-and therefore the package-allows flexible variance-covariance modeling for multivariate meta-analysis models and univariate network meta-analysis models. Complicated computing for these models has prevented their widespread adoption. The package also provides functions to generate relevant plots and perform statistical inferences like model assessments. Use cases are demonstrated using two real data sets contained in <b>metapack</b>.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"14 3","pages":"142-161"},"PeriodicalIF":2.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10168678/pdf/nihms-1894279.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9820786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R JournalPub Date : 2022-06-01Epub Date: 2022-10-10DOI: 10.32614/rj-2022-026
Jiahui Xu, Liying Luo
{"title":"APCI: An R and Stata Package for Visualizing and Analyzing Age-Period-Cohort Data.","authors":"Jiahui Xu, Liying Luo","doi":"10.32614/rj-2022-026","DOIUrl":"10.32614/rj-2022-026","url":null,"abstract":"<p><p>Social scientists have frequently attempted to assess the relative contribution of age, period, and cohort variables to the overall trend in an outcome. We develop an R package <b>APCI</b> (and Stata command apci) to implement the age-period-cohort-interaction (APC-I) model for estimating and testing age, period, and cohort patterns in various types of outcomes for pooled cross-sectional data and multi-cohort panel data. Package <b>APCI</b> also provides a set of functions for visualizing the data and modeling results. We demonstrate the usage of package <b>APCI</b> with empirical data from the Current Population Survey. We show that package <b>APCI</b> provides useful visualization and analytical tools for understanding age, period, and cohort trends in various types of outcomes.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"14 2","pages":"77-95"},"PeriodicalIF":2.3,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237519/pdf/nihms-1897512.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9939343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}