Journal of Statistical Software最新文献_第10页

Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python Bambi:一个用Python拟合贝叶斯线性模型的简单接口

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-12-19 DOI: 10.18637/jss.v103.i15

Tom'as Capretto, Camen Piho, Ravi Kumar, Jacob Westfall, T. Yarkoni, O. A. Martin

引用次数: 40

Continuous Ordinal Regression for Analysis of Visual Analogue Scales: The R Package ordinalCont 视觉模拟尺度分析的连续序数回归:R包序数控制

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-12-05 DOI: 10.18637/jss.v096.i08

M. Manuguerra, G. Heller, Jun Ma

引用次数: 17

fastnet: An R Package for Fast Simulation and Analysis of Large-Scale Social Networks fastnet:一个用于大规模社会网络快速模拟和分析的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-12-05 DOI: 10.2139/ssrn.3121725

Xu Dong, Luis E. Castro, N. I. Shaikh

引用次数: 3

Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package R中离散选择实验的最优设计生成:标识包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-11-29 DOI: 10.18637/jss.v096.i03

Frits Traets, Danielle Sanchez, M. Vandebroek

引用次数: 23

Performing Parallel Monte Carlo and Moment Equations Methods for Itô and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc 执行平行蒙特卡罗和力矩方程方法Itô和Stratonovich随机微分系统:R包模拟。DiffProc

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-11-29 DOI: 10.18637/jss.v096.i02

A. Guidoum, Kamal Boukhetala

{"title":"Performing Parallel Monte Carlo and Moment Equations Methods for Itô and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc","authors":"A. Guidoum, Kamal Boukhetala","doi":"10.18637/jss.v096.i02","DOIUrl":"https://doi.org/10.18637/jss.v096.i02","url":null,"abstract":"We introduce Sim.DiffProc, an R package for symbolic and numerical computations on scalar and multivariate systems of stochastic differential equations (SDEs). It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of these systems in both forms, Ito and Stratonovich. One of Sim.DiffProc key features is to implement the Monte Carlo method for the iterative evaluation and approximation of an interesting quantity at a fixed time on SDEs with parallel computing, on multiple processors on a single machine or a cluster of computers, which is an important tool to improve capacity and speed-up calculations. We also provide an easy-to-use interface for symbolic calculation and numerical approximation of the first and central second-order moments of SDEs (i.e., mean, variance and covariance), by solving a system of ordinary differential equations, which yields insights into the dynamics of stochastic systems. The final result object of Monte Carlo and moment equations can be derived and presented in terms of LATEX math expressions and visualized in terms of LATEX tables. Furthermore, we illustrate various features of the package by proposing a general bivariate nonlinear dynamic system of Haken-Zwanzig, driven by additive, linear and nonlinear multiplicative noises. In addition, we consider the particular case of a scalar SDE driven by three independent Wiener processes. The Monte Carlo simulation thereof is obtained through a transformation to a system of three equations. We also study some important applications of SDEs in different fields.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"123 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2020-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75809179","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

Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package 基于基因检测R基因-基因相互作用的方法:GeneGeneInteR包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-10-12 DOI: 10.18637/jss.v095.i12

M. Emily, Nicolas Sounac, F. Kroell, Magalie Houée-Bigot

{"title":"Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package","authors":"M. Emily, Nicolas Sounac, F. Kroell, Magalie Houée-Bigot","doi":"10.18637/jss.v095.i12","DOIUrl":"https://doi.org/10.18637/jss.v095.i12","url":null,"abstract":"GeneGeneInteR is an R package dedicated to the detection of an association between a case-control phenotype and the interaction between two sets of biallelic markers (single nucleotide polymorphisms or SNPs) in case-control genome-wide associations studies. The development of statistical procedures for searching gene-gene interaction at the SNP-set level has indeed recently grown in popularity as these methods confer advantage in both statistical power and biological interpretation. However, all these methods have been implemented in home made softwares that are for most of them available only on request to the authors and at best have a web interface. Since the implementation of these methods is not straightforward, there is a need for a user-friendly tool to perform gene-based genegene interaction. The purpose of GeneGeneInteR is to propose a collection of tools for all the steps involved in gene-based gene-gene interaction testing in case-control association studies. Illustrated by an example of a dataset related to rheumatoid arthritis, this paper details the implementation of the functions available in GeneGeneInteR to perform an analysis of a collection of SNP sets. Such an analysis aims at addressing the complete statistical pipeline going from data importation to the visualization of the results through data manipulation and statistical analysis.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"33 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77796451","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: 3

Estimation of Random Utility Models in R: The mlogit Package 随机实用模型在R中的估计:mlogit包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-10-07 DOI: 10.18637/jss.v095.i11

Y. Croissant

引用次数: 96

Pseudo-Ranks: How to Calculate Them Efficiently in R 伪秩:如何在R中有效地计算它们

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-10-07 DOI: 10.18637/jss.v095.c01

Martin Happ, G. Zimmermann, E. Brunner, A. Bathke

引用次数: 6

survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling 生存分析用于健康经济评价和成本-效果模型

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-10-07 DOI: 10.18637/jss.v095.i14

G. Baio

引用次数: 19

Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R 各种通用方差:一个面向对象的聚类协方差在R中的实现

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2020-10-07 DOI: 10.18637/JSS.V095.I01

A. Zeileis, Susanne Köll, N. Graham

{"title":"Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R","authors":"A. Zeileis, Susanne Köll, N. Graham","doi":"10.18637/JSS.V095.I01","DOIUrl":"https://doi.org/10.18637/JSS.V095.I01","url":null,"abstract":"Clustered covariances or clustered standard errors are very widely used to account for correlated or clustered data, especially in economics, political sciences, or other social sciences. They are employed to adjust the inference following estimation of a standard least-squares regression or generalized linear model estimated by maximum likelihood. Although many publications just refer to \"the\" clustered standard errors, there is a surprisingly wide variety of clustered covariances particularly due to different flavors of bias corrections. Furthermore, while the linear regression model is certainly the most important application case, the same strategies can be employed in more general models (e.g. for zero-inflated, censored, or limited responses). In R, functions for covariances in clustered or panel models have been somewhat scattered or available only for certain modeling functions, notably the (generalized) linear regression model. In contrast, an object-oriented approach to \"robust\" covariance matrix estimation - applicable beyond lm() and glm() - is available in the sandwich package but has been limited to the case of cross-section or time series data. Now, this shortcoming has been corrected in sandwich (starting from version 2.4.0): Based on methods for two generic functions (estfun() and bread()), clustered and panel covariances are now provided in vcovCL(), vcovPL(), and vcovPC(). These are directly applicable to models from many packages, e.g., including MASS, pscl, countreg, betareg, among others. Some empirical illustrations are provided as well as an assessment of the methods' performance in a simulation study.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"41 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77953816","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: 290