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mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models 课程5:使用高斯有限混合模型的聚类、分类和密度估计
IF 2.1 4区 计算机科学
R Journal Pub Date : 2016-08-01 DOI: 10.32614/RJ-2016-021
L. Scrucca, Michael Fop, T. B. Murphy, A. Raftery
{"title":"mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models","authors":"L. Scrucca, Michael Fop, T. B. Murphy, A. Raftery","doi":"10.32614/RJ-2016-021","DOIUrl":"https://doi.org/10.32614/RJ-2016-021","url":null,"abstract":"Finite mixture models are being used increasingly to model a wide variety of random phenomena for clustering, classification and density estimation. mclust is a powerful and popular package which allows modelling of data as a Gaussian finite mixture with different covariance structures and different numbers of mixture components, for a variety of purposes of analysis. Recently, version 5 of the package has been made available on CRAN. This updated version adds new covariance structures, dimension reduction capabilities for visualisation, model selection criteria, initialisation strategies for the EM algorithm, and bootstrap-based inference, making it a full-featured R package for data analysis via finite mixture modelling.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"8 1 1","pages":"289-317"},"PeriodicalIF":2.1,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1758
fslr: Connecting the FSL Software with R. fslr：用 R 连接 FSL 软件
IF 2.1 4区 计算机科学
R Journal Pub Date : 2015-06-01
John Muschelli, Elizabeth Sweeney, Martin Lindquist, Ciprian Crainiceanu
{"title":"fslr: Connecting the FSL Software with R.","authors":"John Muschelli, Elizabeth Sweeney, Martin Lindquist, Ciprian Crainiceanu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We present the package <b>fslr</b>, a set of R functions that interface with FSL (FMRIB Software Library), a commonly-used open-source software package for processing and analyzing neuroimaging data. The <b>fslr</b> package performs operations on 'nifti' image objects in R using command-line functions from FSL, and returns R objects back to the user. <b>fslr</b> allows users to develop image processing and analysis pipelines based on FSL functionality while interfacing with the functionality provided by R. We present an example of the analysis of structural magnetic resonance images, which demonstrates how R users can leverage the functionality of FSL without switching to shell commands.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"7 1","pages":"163-175"},"PeriodicalIF":2.1,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911193/pdf/nihms-792376.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34664393","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}
引用次数: 0
fslr: Connecting the FSL Software with R fslr:连接FSL软件与R
IF 2.1 4区 计算机科学
R Journal Pub Date : 2015-01-01 DOI: 10.32614/RJ-2015-013
J. Muschelli, E. Sweeney, M. Lindquist, C. Crainiceanu
{"title":"fslr: Connecting the FSL Software with R","authors":"J. Muschelli, E. Sweeney, M. Lindquist, C. Crainiceanu","doi":"10.32614/RJ-2015-013","DOIUrl":"https://doi.org/10.32614/RJ-2015-013","url":null,"abstract":"We present the package fslr, a set of R functions that interface with FSL (FMRIB Software Library), a commonly-used open-source software package for processing and analyzing neuroimaging data. The fslr package performs operations on 'nifti' image objects in R using command-line functions from FSL, and returns R objects back to the user. fslr allows users to develop image processing and analysis pipelines based on FSL functionality while interfacing with the functionality provided by R. We present an example of the analysis of structural magnetic resonance images, which demonstrates how R users can leverage the functionality of FSL without switching to shell commands.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"12 1","pages":"163-175"},"PeriodicalIF":2.1,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 30
Stratified Weibull Regression Model for Interval-Censored Data. 区间截尾数据的分层威布尔回归模型。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2014-06-01
Xiangdong Gu, David Shapiro, Michael D Hughes, Raji Balasubramanian
{"title":"Stratified Weibull Regression Model for Interval-Censored Data.","authors":"Xiangdong Gu, David Shapiro, Michael D Hughes, Raji Balasubramanian","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and implement a new R package <b>straweib</b>, for fitting a stratified Weibull model appropriate for interval censored outcomes. We illustrate the R package <b>straweib</b> by analyzing data from a longitudinal oral health study on the timing of the emergence of permanent teeth in 4430 children.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"6 1","pages":"31-40"},"PeriodicalIF":2.1,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4729374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72211871","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}
引用次数: 0
brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data. brainR:交互式三维和四维图像的高分辨率神经图像数据。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2014-06-01
John Muschelli, Elizabeth Sweeney, Ciprian Crainiceanu
{"title":"brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data.","authors":"John Muschelli,&nbsp;Elizabeth Sweeney,&nbsp;Ciprian Crainiceanu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We provide software tools for displaying and publishing interactive 3-dimensional (3D) and 4-dimensional (4D) figures to html webpages, with examples of high-resolution brain imaging. Our framework is based in the R statistical software using the <b>rgl</b> package, a 3D graphics library. We build on this package to allow manipulation of figures including rotation and translation, zooming, coloring of brain substructures, adjusting transparency levels, and addition/or removal of brain structures. The need for better visualization tools of ultra high dimensional data is ever present; we are providing a clean, simple, web-based option. We also provide a package (<b>brainR</b>) for users to readily implement these tools.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"6 1","pages":"41-48"},"PeriodicalIF":2.1,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4911196/pdf/nihms658287.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34601322","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}
引用次数: 0
Stratified Weibull Regression Model for Interval-Censored Data 区间截尾数据的分层威布尔回归模型
IF 2.1 4区 计算机科学
R Journal Pub Date : 2014-06-01 DOI: 10.32614/RJ-2014-003
Xiangdong Gu, D. Shapiro, M. Hughes, R. Balasubramanian
{"title":"Stratified Weibull Regression Model for Interval-Censored Data","authors":"Xiangdong Gu, D. Shapiro, M. Hughes, R. Balasubramanian","doi":"10.32614/RJ-2014-003","DOIUrl":"https://doi.org/10.32614/RJ-2014-003","url":null,"abstract":"Interval censored outcomes arise when a silent event of interest is known to have occurred within a specific time period determined by the times of the last negative and first positive diagnostic tests. There is a rich literature on parametric and non-parametric approaches for the analysis of interval-censored outcomes. A commonly used strategy is to use a proportional hazards (PH) model with the baseline hazard function parameterized. The proportional hazards assumption can be relaxed in stratified models by allowing the baseline hazard function to vary across strata defined by a subset of explanatory variables. In this paper, we describe and implement a new R package straweib, for fitting a stratified Weibull model appropriate for interval censored outcomes. We illustrate the R package straweib by analyzing data from a longitudinal oral health study on the timing of the emergence of permanent teeth in 4430 children.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"6 1 1","pages":"31-40"},"PeriodicalIF":2.1,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data brainR:交互式三维和四维图像的高分辨率神经图像数据
IF 2.1 4区 计算机科学
R Journal Pub Date : 2014-06-01 DOI: 10.32614/RJ-2014-004
J. Muschelli, E. Sweeney, C. Crainiceanu
{"title":"brainR: Interactive 3 and 4D Images of High Resolution Neuroimage Data","authors":"J. Muschelli, E. Sweeney, C. Crainiceanu","doi":"10.32614/RJ-2014-004","DOIUrl":"https://doi.org/10.32614/RJ-2014-004","url":null,"abstract":"We provide software tools for displaying and publishing interactive 3-dimensional (3D) and 4-dimensional (4D) figures to html webpages, with examples of high-resolution brain imaging. Our framework is based in the R statistical software using the rgl package, a 3D graphics library. We build on this package to allow manipulation of figures including rotation and translation, zooming, coloring of brain substructures, adjusting transparency levels, and addition/or removal of brain structures. The need for better visualization tools of ultra high dimensional data is ever present; we are providing a clean, simple, web-based option. We also provide a package (brainR) for users to readily implement these tools.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"6 1 1","pages":"41-48"},"PeriodicalIF":2.1,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
RNetCDF – A Package for Reading and Writing NetCDF Datasets 一个用于读写NetCDF数据集的包
IF 2.1 4区 计算机科学
R Journal Pub Date : 2013-12-01 DOI: 10.7892/BORIS.47220
Pavel Michna, Milton Woods
{"title":"RNetCDF – A Package for Reading and Writing NetCDF Datasets","authors":"Pavel Michna, Milton Woods","doi":"10.7892/BORIS.47220","DOIUrl":"https://doi.org/10.7892/BORIS.47220","url":null,"abstract":"This paper describes the RNetCDF package (version 1.6), an interface for reading and writing files in Unidata NetCDF format, and gives an introduction to the NetCDF file format. NetCDF is a machine independent binary file format which allows storage of different types of array based data, along with short metadata descriptions. The package presented here allows access to the most \u0000important functions of the NetCDF C-interface for reading, writing, and modifying NetCDF datasets. In this paper, we present a short overview on the NetCDF file format and show usage examples of the package.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"92 1","pages":"29-36"},"PeriodicalIF":2.1,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79429829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
Fast Pure R Implementation of GEE: Application of the Matrix Package GEE的快速纯R实现:矩阵包的应用
IF 2.1 4区 计算机科学
R Journal Pub Date : 2013-06-01 DOI: 10.32614/RJ-2013-017
Lee S McDaniel, Nicholas C Henderson, P. Rathouz
{"title":"Fast Pure R Implementation of GEE: Application of the Matrix Package","authors":"Lee S McDaniel, Nicholas C Henderson, P. Rathouz","doi":"10.32614/RJ-2013-017","DOIUrl":"https://doi.org/10.32614/RJ-2013-017","url":null,"abstract":"Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"5 1 1","pages":"181-187"},"PeriodicalIF":2.1,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69958576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 47
Fast Pure R Implementation of GEE: Application of the Matrix Package. GEE的快速纯R实现:矩阵包的应用。
IF 2.1 4区 计算机科学
R Journal Pub Date : 2013-06-01
Lee S McDaniel, Nicholas C Henderson, Paul J Rathouz
{"title":"Fast Pure R Implementation of GEE: Application of the Matrix Package.","authors":"Lee S McDaniel,&nbsp;Nicholas C Henderson,&nbsp;Paul J Rathouz","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, <b>geeM</b>, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the <b>Matrix</b> package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of <b>geeM</b>, which is not much worse than C implementations like <b>geepack</b> and <b>gee</b> on small data sets and faster on large data sets.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"5 1","pages":"181-187"},"PeriodicalIF":2.1,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4289620/pdf/nihms-607237.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32974591","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}
引用次数: 0
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