Ó. Lado-Baleato, J. Roca-Pardiñas, C. Cadarso-Suárez, Francisco Gude
{"title":"Refreg: An R Package for Estimating Conditional Reference Regions","authors":"Ó. Lado-Baleato, J. Roca-Pardiñas, C. Cadarso-Suárez, Francisco Gude","doi":"10.32614/rj-2022-029","DOIUrl":"https://doi.org/10.32614/rj-2022-029","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"11 1","pages":"136-152"},"PeriodicalIF":0.0,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74517038","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":"Power and Sample Size for Longitudinal Models in R - The longpower Package and Shiny App","authors":"S. Iddi, M. Donohue","doi":"10.32614/rj-2022-022","DOIUrl":"https://doi.org/10.32614/rj-2022-022","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"145 1","pages":"264-282"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86213557","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}
kStatistics is a package in R that serves as a unified framework for estimating univariate and multivariate cumulants as well as products of univariate and multivariate cumulants of a random sample, using unbiased estimators with minimum variance. The main computational machinery of kStatistics is an algorithm for computing multi-index partitions. The same algorithm underlies the general-purpose multivariate Fa`a di Bruno's formula, which has been therefore included in the last release of the package. This formula gives the coefficients of formal power series compositions as well as the partial derivatives of multivariable function compositions. One of the most significant applications of this formula is the possibility to generate many well-known polynomial families as special cases. So, in the package, there are special functions for generating very popular polynomial families, such as the Bell polynomials. However further families can be obtained, for suitable choices of the formal power series involved in the composition or when suitable symbolic strategies are employed. In both cases, we give examples on how to modify the R codes of the package to accomplish this task. Future developments are addressed at the end of the paper.
kStatistics是R中的一个包，它作为一个统一的框架，用于估计单变量和多变量累积量，以及随机样本的单变量和多变量累积量的乘积，使用方差最小的无偏估计量。kStatistics的主要计算机制是用于计算多索引分区的算法。相同的算法是通用多元Fa ' a di Bruno公式的基础，因此该公式已包含在软件包的最后一个版本中。这个公式给出了形式幂级数组合的系数以及多变量函数组合的偏导数。这个公式最重要的应用之一是可以生成许多众所周知的多项式族作为特殊情况。在这个包里，有一些特殊的函数用来生成非常流行的多项式族，比如贝尔多项式。然而，如果选择合适的形式幂级数或采用合适的符号策略，则可以得到更多的族。在这两种情况下，我们都给出了如何修改包的R代码来完成此任务的示例。论文最后对未来的发展进行了展望。
{"title":"kStatistics: Unbiased Estimates of Joint Cumulant Products from the Multivariate Faà Di Bruno's Formula","authors":"E. Nardo, G. Guarino","doi":"10.32614/rj-2022-033","DOIUrl":"https://doi.org/10.32614/rj-2022-033","url":null,"abstract":"kStatistics is a package in R that serves as a unified framework for estimating univariate and multivariate cumulants as well as products of univariate and multivariate cumulants of a random sample, using unbiased estimators with minimum variance. The main computational machinery of kStatistics is an algorithm for computing multi-index partitions. The same algorithm underlies the general-purpose multivariate Fa`a di Bruno's formula, which has been therefore included in the last release of the package. This formula gives the coefficients of formal power series compositions as well as the partial derivatives of multivariable function compositions. One of the most significant applications of this formula is the possibility to generate many well-known polynomial families as special cases. So, in the package, there are special functions for generating very popular polynomial families, such as the Bell polynomials. However further families can be obtained, for suitable choices of the formal power series involved in the composition or when suitable symbolic strategies are employed. In both cases, we give examples on how to modify the R codes of the package to accomplish this task. Future developments are addressed at the end of the paper.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"30 1","pages":"209-229"},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86003074","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}
Although linear autoregressive models are useful to practitioners in different fields, often a nonlinear specification would be more appropriate in time series analysis. In general, there are many alternative approaches to nonlinearity modelling, one consists in assuming multiple regimes. Among the possible specifications that account for regime changes in the multivariate framework, smooth transition models are the most general, since they nest both linear and threshold autoregressive models. This paper introduces the starvars package which estimates and predicts the Vector Logistic Smooth Transition model in a very general setting which also includes predetermined variables. In comparison to the existing R packages, starvars offers the estimation of the Vector Smooth Transition model both by maximum likelihood and nonlinear least squares. The package allows also to test for nonlinearity in a multivariate setting and detect the presence of common breaks. Furthermore, the package computes multi-step-ahead forecasts. Finally, an illustration with financial time series is provided to show its usage.
{"title":"starvars: An R Package for Analysing Nonlinearities in Multivariate Time Series","authors":"Andrea Bucci, Giulio Palomba, E. Rossi","doi":"10.32614/rj-2022-018","DOIUrl":"https://doi.org/10.32614/rj-2022-018","url":null,"abstract":"Although linear autoregressive models are useful to practitioners in different fields, often a nonlinear specification would be more appropriate in time series analysis. In general, there are many alternative approaches to nonlinearity modelling, one consists in assuming multiple regimes. Among the possible specifications that account for regime changes in the multivariate framework, smooth transition models are the most general, since they nest both linear and threshold autoregressive models. This paper introduces the starvars package which estimates and predicts the Vector Logistic Smooth Transition model in a very general setting which also includes predetermined variables. In comparison to the existing R packages, starvars offers the estimation of the Vector Smooth Transition model both by maximum likelihood and nonlinear least squares. The package allows also to test for nonlinearity in a multivariate setting and detect the presence of common breaks. Furthermore, the package computes multi-step-ahead forecasts. Finally, an illustration with financial time series is provided to show its usage.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"66 1","pages":"208-226"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87578756","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}
Allison M. Horst, Alison Presmanes Hill, K. Gorman
{"title":"Palmer Archipelago Penguins Data in the palmerpenguins R Package - An Alternative to Anderson's Irises","authors":"Allison M. Horst, Alison Presmanes Hill, K. Gorman","doi":"10.32614/rj-2022-020","DOIUrl":"https://doi.org/10.32614/rj-2022-020","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"146 1","pages":"244-254"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82895402","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":"Revisiting Historical Bar Graphics on Epidemics in the Era of R ggplot2","authors":"Sami Aldag, Dogukan Topcuoglu, G. Inan","doi":"10.32614/rj-2022-010","DOIUrl":"https://doi.org/10.32614/rj-2022-010","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"320 1","pages":"146-166"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83835892","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":"tvReg: Time-varying Coefficients in Multi-Equation Regression in R","authors":"Isabel Casas, R. Fernández-Casal","doi":"10.32614/rj-2022-002","DOIUrl":"https://doi.org/10.32614/rj-2022-002","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"92 1","pages":"79-100"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90508946","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":"blindrecalc - An R Package for Blinded Sample Size Recalculation","authors":"Lukas Baumann, Maximilian Pilz, M. Kieser","doi":"10.32614/rj-2022-001","DOIUrl":"https://doi.org/10.32614/rj-2022-001","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"45 1","pages":"137-145"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86930740","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}
C. M. Tso, M. Hollaway, Rebecca Killick, P. Henrys, Don Monteith, J. Watkins, Gordon S. Blair
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{"title":"Advancing Reproducible Research by Publishing R Markdown Notebooks as Interactive Sandboxes Using the learnr Package","authors":"C. M. Tso, M. Hollaway, Rebecca Killick, P. Henrys, Don Monteith, J. Watkins, Gordon S. Blair","doi":"10.32614/rj-2022-021","DOIUrl":"https://doi.org/10.32614/rj-2022-021","url":null,"abstract":",","PeriodicalId":20974,"journal":{"name":"R J.","volume":"5 1","pages":"255-263"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72636373","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":"A Software Tool For Sparse Estimation Of A General Class Of High-dimensional GLMs","authors":"H. Pazira, L. Augugliaro, E. Wit","doi":"10.32614/rj-2022-008","DOIUrl":"https://doi.org/10.32614/rj-2022-008","url":null,"abstract":"","PeriodicalId":20974,"journal":{"name":"R J.","volume":"2014 1","pages":"34-53"},"PeriodicalIF":0.0,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86506931","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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