Journal of Statistical Software最新文献_第2页

Broken Stick Model for Irregular Longitudinal Data 不规则纵向数据的断棒模型

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v106.i07

S. Buuren

引用次数: 1

Regression Modeling for Recurrent Events Possibly with an Informative Terminal Event Using R Package reReg. 使用 R 软件包 reReg 对可能带有信息终端事件的重复事件进行回归建模。

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 Epub Date: 2023-01-28 DOI: 10.18637/jss.v105.i05

Sy Han Chiou, Gongjun Xu, Jun Yan, Chiung-Yu Huang

引用次数: 0

DataFrames.jl: Flexible and Fast Tabular Data in Julia DataFrames。灵活和快速的表格数据在Julia

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i04

Milan Bouchet-Valat, Bogumi Kamiński

引用次数: 2

Modeling Population Growth in R with the biogrowth Package 用生物生长包在R中模拟种群增长

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i01

Alberto Garre, Jeroen Koomen, Heidy M. W. den Besten, Marcel H. Zwietering

{"title":"Modeling Population Growth in R with the biogrowth Package","authors":"Alberto Garre, Jeroen Koomen, Heidy M. W. den Besten, Marcel H. Zwietering","doi":"10.18637/jss.v107.i01","DOIUrl":"https://doi.org/10.18637/jss.v107.i01","url":null,"abstract":"The growth of populations is of interest in a broad variety of fields, such as epidemiology, economics or biology. Although a large variety of growth models are available in the scientific literature, their application usually requires advanced knowledge of mathematical programming and statistical inference, especially when modelling growth under dynamic environmental conditions. This article presents the biogrowth package for R, which implements functions for modelling the growth of populations. It can predict growth under static or dynamic environments, considering the effect of an arbitrary number of environmental factors. Moreover, it can be used to fit growth models to data gathered under static or dynamic environmental conditions. The package allows the user to fix any model parameter prior to the fit, an approach that can mitigate identifiability issues associated to growth models. The package includes common S3 methods for visualization and statistical analysis (summary of the fit, predictions, . . . ), easing result interpretation. It also includes functions for model comparison/selection. We illustrate the functions in biogrowth using examples from food science and economy.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312289","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

carat: An R Package for Covariate-Adaptive Randomization in Clinical Trials carat:临床试验中协变量自适应随机化的R包

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i02

Wei Ma, Xiaoqing Ye, Fuyi Tu, Feifang Hu

{"title":"carat: An R Package for Covariate-Adaptive Randomization in Clinical Trials","authors":"Wei Ma, Xiaoqing Ye, Fuyi Tu, Feifang Hu","doi":"10.18637/jss.v107.i02","DOIUrl":"https://doi.org/10.18637/jss.v107.i02","url":null,"abstract":"Covariate-adaptive randomization is gaining popularity in clinical trials because they enable the generation of balanced allocations with respect to covariates. Over the past decade, substantial progress has been made in both new innovative randomization procedures and the theoretical properties of associated inferences. However, these results are scattered across the literature, and a single tool kit does not exist for use by clinical trial practitioners and researchers to conduct and evaluate these methods. The R package carat is proposed to address this need. It facilitates a broad range of covariate-adaptive randomization and testing procedures, such as the most common and classical methods, and also reflects recent developments in the field. The package contains comprehensive evaluation and comparison tools for use in both randomization procedures and tests. This enables power analysis to be conducted to assist the planning of a covariate-adaptive clinical trial. The package also implements a command-line interface to allow for an interactive allocation procedure, which is typically the case in real-world applications. In this paper, the features and functionalities of carat are presented.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135312292","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: 0

cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values 一个带有删减值和缺失值的条件图形Lasso推理的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v105.i01

L. Augugliaro, G. Sottile, E. C. Wit, V. Vinciotti

{"title":"cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values","authors":"L. Augugliaro, G. Sottile, E. C. Wit, V. Vinciotti","doi":"10.18637/jss.v105.i01","DOIUrl":"https://doi.org/10.18637/jss.v105.i01","url":null,"abstract":"Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse conditional Gaussian graphical models with potentially missing or censored data. The method employs an efficient expectation-maximization estimation of an l1-penalized likelihood via a block-coordinate descent algorithm. The package has a user-friendly data manipulation interface. It estimates a solution path and includes various automatic selection algorithms for the two l1 tuning parameters, associated with the sparse precision matrix and sparse regression coefficients, respectively. The package pays particular attention to the visualization of the results, both by means of marginal tables and figures, and of the inferred conditional independence graphs. This package provides a unique and computational efficient implementation of a conditional Gaussian graphical model that is able to deal with the additional complications of missing and censored data. As such it constitutes an important contribution for empirical scientists wishing to detect sparse structures in high-dimensional data.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86751288","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: 0

Elastic Net Regularization Paths for All Generalized Linear Models. 所有广义线性模型的弹性网正则化路径。

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 Epub Date: 2023-03-23 DOI: 10.18637/jss.v106.i01

J Kenneth Tay, Balasubramanian Narasimhan, Trevor Hastie

引用次数: 0

Panel Data Visualization in R (panelView) and Stata (panelview) R (panelView)和Stata (panelView)中的面板数据可视化

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i07

Hongyu Mou, Licheng Liu, Yiqing Xu

引用次数: 1

Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package. 考虑过去不确定性的年总和生育率概率估计和预测：bayesTFR R 软件包的重大更新。

IF 5.4 2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 Epub Date: 2023-03-26 DOI: 10.18637/jss.v106.i08

Peiran Liu, Hana Ševčíková, Adrian E Raftery

{"title":"Probabilistic Estimation and Projection of the Annual Total Fertility Rate Accounting for Past Uncertainty: A Major Update of the bayesTFR R Package.","authors":"Peiran Liu, Hana Ševčíková, Adrian E Raftery","doi":"10.18637/jss.v106.i08","DOIUrl":"10.18637/jss.v106.i08","url":null,"abstract":"The bayesTFR package for R provides a set of functions to produce probabilistic projections of the total fertility rates (TFR) for all countries, and is widely used, including as part of the basis for the UN's official population projections for all countries. Liu and Raftery (2020) extended the theoretical model by adding a layer that accounts for the past TFR estimation uncertainty. A major update of bayesTFR implements the new extension. Moreover, a new feature of producing annual TFR estimation and projections extends the existing functionality of estimating and projecting for five-year time periods. An additional autoregressive component has been developed in order to account for the larger autocorrelation in the annual version of the model. This article summarizes the updated model, describes the basic steps to generate probabilistic estimation and projections under different settings, compares performance, and provides instructions on how to summarize, visualize and diagnose the model results.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83223922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

GMM Estimators for Binary Spatial Models in R 二元空间模型的GMM估计

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i08

Gianfranco Piras, Mauricio Sarrias

引用次数: 0