Journal of Statistical Software最新文献_第3页

Robust Mediation Analysis: The R Package robmed 稳健中介分析:R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-02-24 DOI: 10.18637/jss.v103.i13

A. Alfons, N. Ateş, P. Groenen

{"title":"Robust Mediation Analysis: The R Package robmed","authors":"A. Alfons, N. Ateş, P. Groenen","doi":"10.18637/jss.v103.i13","DOIUrl":"https://doi.org/10.18637/jss.v103.i13","url":null,"abstract":"Mediation analysis is one of the most widely used statistical techniques in the social, behavioral, and medical sciences. Mediation models allow to study how an independent variable affects a dependent variable indirectly through one or more intervening variables, which are called mediators. The analysis is often carried out via a series of linear regressions, in which case the indirect effects can be computed as products of coefficients from those regressions. Statistical significance of the indirect effects is typically assessed via a bootstrap test based on ordinary least-squares estimates. However, this test is sensitive to outliers or other deviations from normality assumptions, which poses a serious threat to empirical testing of theory about mediation mechanisms. The R package robmed implements a robust procedure for mediation analysis based on the fast-and-robust bootstrap methodology for robust regression estimators, which yields reliable results even when the data deviate from the usual normality assumptions. Various other procedures for mediation analysis are included in package robmed as well. Moreover, robmed introduces a new formula interface that allows to specify mediation models with a single formula, and provides various plots for diagnostics or visual representation of the results.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"40 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74051967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 6

Hierarchical Clustering with Contiguity Constraint in R 具有邻近约束的分层聚类

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.i07

G. Guénard, P. Legendre

{"title":"Hierarchical Clustering with Contiguity Constraint in R","authors":"G. Guénard, P. Legendre","doi":"10.18637/jss.v103.i07","DOIUrl":"https://doi.org/10.18637/jss.v103.i07","url":null,"abstract":"This article presents a new implementation of hierarchical clustering for the R language that allows one to apply spatial or temporal contiguity constraints during the clustering process. The need for contiguity constraint arises, for instance, when one wants to partition a map into different domains of similar physical conditions, identify discontinuities in time series, group regional administrative units with respect to their performance, and so on. To increase computation efficiency, we programmed the core functions in plain C . The result is a new R function, constr.hclust , which is distributed in package adespatial . The program implements the general agglomerative hierarchical clustering algorithm described by Lance and Williams (1966; 1967), with the particularity of allowing only clusters that are contiguous in geographic space or along time to fuse at any given step. Contiguity can be defined with respect to space or time. Information about spatial contiguity is provided by a connection network among sites, with edges describing the links between connected sites. Clustering with a temporal contiguity constraint is also known as chronological clustering. Information on temporal contiguity can be implicitly provided as the rank positions of observations in the time series. The implementation was mirrored on that found in the hierarchical clustering function hclust of the standard R package stats ( R Core Team 2022). We transcribed that function from Fortran to C and added the functionality to apply constraints when running the function. The implementation is efficient. It is limited mainly by input/output access as massive amounts of memory are potentially needed to store copies of the dissimilarity matrix and update its elements when analyzing large problems. We provided R computer code for plotting results for numbers of clusters.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"2013 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87740464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

stringi: Fast and Portable Character String Processing in R 在R中快速和可移植的字符串处理

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.i02

M. Gagolewski

引用次数: 26

Learning Base R (2nd Edition) 学习基础R(第二版)

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.b01

James E. Helmreich

引用次数: 0

bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R Boltzmann Bayes学习器在R中的离散预测的高维推理

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v101.i05

J. Woo, Jinhua Wang

引用次数: 0

TransModel: An R Package for Linear Transformation Model with Censored Data TransModel:一个带有删节数据的线性变换模型的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v101.i09

Jie Zhou, Jiajia Zhang, Wenbin Lu

引用次数: 1

Spbsampling: An R Package for Spatially Balanced Sampling Spbsampling:一个空间平衡采样的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.c02

Francesco Pantalone, R. Benedetti, Federica Pierismoni

{"title":"Spbsampling: An R Package for Spatially Balanced Sampling","authors":"Francesco Pantalone, R. Benedetti, Federica Pierismoni","doi":"10.18637/jss.v103.c02","DOIUrl":"https://doi.org/10.18637/jss.v103.c02","url":null,"abstract":"The basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart. Therefore, it should be desirable to select a sample well spread over the population of interest, or a spatially balanced sample . This situation is easily understood in, among many others, environmental, geological, biological, and agricultural surveys, where usually the main feature of the population is to be geo-referenced. Since traditional sampling designs generally do not exploit the spatial features and since it is desirable to take into account the information regarding spatial dependence, several sampling designs have been developed in order to achieve this objective. In this paper, we present the R package Spbsampling , which provides functions in order to perform three specific sampling designs that pursue the aforementioned purpose. In particular, these sampling designs achieve spatially balanced samples using a summary index of the distance matrix. In this sense, the applicability of the package is much wider, as a distance matrix can be defined for units according to variables different than geographical coordinates.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"26 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80110451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 1

Feller-Pareto and Related Distributions: Numerical Implementation and Actuarial Applications 费勒-帕累托及相关分布:数值实现和精算应用

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.i06

Christophe Dutang, Vincent Goulet, Nicholas Langevin

引用次数: 2

covsim: An R Package for Simulating Non-Normal Data for Structural Equation Models Using Copulas covsim:一个用copula模拟结构方程模型非正态数据的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v102.i03

Steffen Grønneberg, Njål Foldnes, Katerina M. Marcoulides

引用次数: 5

The poolr Package for Combining Independent and Dependent p Values 用于组合独立和依赖p值的池包