{"title":"NScluster:一个使用OpenMP对聚类点过程模型进行最大手掌似然估计的R包","authors":"U. Tanaka, Masami Saga, Junji Nakano","doi":"10.18637/jss.v098.i06","DOIUrl":null,"url":null,"abstract":"NScluster is an R package used for simulation and parameter estimation for NeymanScott cluster point process models and their extensions. For parameter estimation, NScluster uses the maximum Palm likelihood estimation procedure. As some estimation procedures proposed herein require heavy calculation, NScluster can use parallel computation via OpenMP and achieve significant speedup in some cases. In this paper, we discuss results obtained using a laptop PC and a shared memory supercomputer. In addition, we examine the performance characteristics of parallel computation via OpenMP.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"NScluster: An R Package for Maximum Palm Likelihood Estimation for Cluster Point Process Models Using OpenMP\",\"authors\":\"U. Tanaka, Masami Saga, Junji Nakano\",\"doi\":\"10.18637/jss.v098.i06\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"NScluster is an R package used for simulation and parameter estimation for NeymanScott cluster point process models and their extensions. For parameter estimation, NScluster uses the maximum Palm likelihood estimation procedure. As some estimation procedures proposed herein require heavy calculation, NScluster can use parallel computation via OpenMP and achieve significant speedup in some cases. In this paper, we discuss results obtained using a laptop PC and a shared memory supercomputer. In addition, we examine the performance characteristics of parallel computation via OpenMP.\",\"PeriodicalId\":17237,\"journal\":{\"name\":\"Journal of Statistical Software\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Software\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.18637/jss.v098.i06\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Software","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.18637/jss.v098.i06","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
NScluster: An R Package for Maximum Palm Likelihood Estimation for Cluster Point Process Models Using OpenMP
NScluster is an R package used for simulation and parameter estimation for NeymanScott cluster point process models and their extensions. For parameter estimation, NScluster uses the maximum Palm likelihood estimation procedure. As some estimation procedures proposed herein require heavy calculation, NScluster can use parallel computation via OpenMP and achieve significant speedup in some cases. In this paper, we discuss results obtained using a laptop PC and a shared memory supercomputer. In addition, we examine the performance characteristics of parallel computation via OpenMP.
期刊介绍:
The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.