2023-4 issue

_articles/RJ-2023-080/RJ-2023-080.R

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+# Generated by `rjournal_pdf_article()` using `knitr::purl()`: do not edit by hand
+# Please edit RJ-2023-080.Rmd to modify this file
+
+

_articles/RJ-2023-080/RJ-2023-080.Rmd

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+---
+title: 'SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data
+  Based on Boosting Method'
+abstract: ' Boosting is a powerful statistical learning method. Its key feature is
+  the ability to derive a strong learner from simple yet weak learners by iteratively
+  updating the learning results. Moreover, boosting algorithms have been employed
+  to do variable selection and estimation for regression models. However, measurement
+  error usually appears in covariates. Ignoring measurement error can lead to biased
+  estimates and wrong inferences. To the best of our knowledge, few packages have
+  been developed to address measurement error and variable selection simultaneously
+  by using boosting algorithms. In this paper, we introduce an R package [SIMEXBoost](https://CRAN.R-project.org/package=SIMEXBoost),
+  which covers some widely used regression models and applies the simulation and extrapolation
+  method to deal with measurement error effects. Moreover, the package [SIMEXBoost](https://CRAN.R-project.org/package=SIMEXBoost)
+  enables us to do variable selection and estimation for high-dimensional data under
+  various regression models. To assess the performance and illustrate the features
+  of the package, we conduct numerical studies. '
+author:
+- name: Li-Pang Chen
+  affiliation: Department of Statistics, National Chengchi University
+  orcid: 0000-0001-5440-5036
+  email: |
+    [email protected]
+  address:
+  - No. 64, Section 2, Zhinan Rd, Wenshan District, Taipei City, 116
+  - Taiwan (R.O.C.)
+- name: Bangxu Qiu
+  affiliation: Department of Statistics, National Chengchi University
+  email: |
+    [email protected]
+  address:
+  - No. 64, Section 2, Zhinan Rd, Wenshan District, Taipei City, 116
+  - Taiwan (R.O.C.)
+date: '2024-04-11'
+date_received: '2022-04-13'
+journal:
+  firstpage: 5
+  lastpage: 20
+volume: 15
+issue: 4
+slug: RJ-2023-080
+packages:
+  cran: ~
+  bioc: ~
+draft: no
+preview: preview.png
+tex_native: yes
+preamble: \input{preamble.tex}
+nocite: |
+  @Agresti:2012,@adabagR,@GLSMER,@Boyd:2004,@Brown:2017,@Carroll:2006,@Chen:2021,@BOOME,@Chen:2020,@Chen:2023,@Chen:2023b,@ChenQiu:2023,@ChenYi:2021,@xgboostR,@SISR,@glmnetR,@gbmR,@GMMBoostR,@Hastie:2008,@gamboostLSSR,@Lawless:2003,@simexR,@mecorR,@SIMEXBoostR,@lightgbmR,@Tibshirani:1996,@bstR,@Wolfson:2011,@simexaftR,@Yi:2017,@augSIMEXR,@Zou:2006,@Zou:2005
+bibliography: SIMEXBoost-Chen.bib
+CTV: ~
+output:
+  rjtools::rjournal_web_article:
+    self_contained: no
+    toc: no
+    legacy_pdf: yes
+
+---
+```{=latex}
+\input{RJ-2023-080-src.tex}
+```

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_articles/RJ-2023-080/RJ-2023-080.tex

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+% !TeX root = RJwrapper.tex
+\title{SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data Based on Boosting Method}
+
+
+\author{by Li-Pang Chen and Bangxu Qiu}
+
+\maketitle
+
+\abstract{%
+Boosting is a powerful statistical learning method. Its key feature is the ability to derive a strong learner from simple yet weak learners by iteratively updating the learning results. Moreover, boosting algorithms have been employed to do variable selection and estimation for regression models. However, measurement error usually appears in covariates. Ignoring measurement error can lead to biased estimates and wrong inferences. To the best of our knowledge, few packages have been developed to address measurement error and variable selection simultaneously by using boosting algorithms. In this paper, we introduce an R package \href{https://CRAN.R-project.org/package=SIMEXBoost}{SIMEXBoost}, which covers some widely used regression models and applies the simulation and extrapolation method to deal with measurement error effects. Moreover, the package \href{https://CRAN.R-project.org/package=SIMEXBoost}{SIMEXBoost} enables us to do variable selection and estimation for high-dimensional data under various regression models. To assess the performance and illustrate the features of the package, we conduct numerical studies.
+}
+
+\input{RJ-2023-080-src.tex}
+
+\hypertarget{refs}{}
+
+\bibliography{SIMEXBoost-Chen.bib}
+
+\address{%
+Li-Pang Chen\\
+Department of Statistics, National Chengchi University\\%
+No.~64, Section 2, Zhinan Rd, Wenshan District, Taipei City, 116\\ Taiwan (R.O.C.)\\
+%
+%
+\textit{ORCiD: \href{https://orcid.org/0000-0001-5440-5036}{0000-0001-5440-5036}}\\%
+\href{mailto:[email protected]}{\nolinkurl{[email protected]}}%
+}
+
+\address{%
+Bangxu Qiu\\
+Department of Statistics, National Chengchi University\\%
+No.~64, Section 2, Zhinan Rd, Wenshan District, Taipei City, 116\\ Taiwan (R.O.C.)\\
+%
+%
+%
+\href{mailto:[email protected]}{\nolinkurl{[email protected]}}%
+}