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

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":"81 1","pages":"0"},"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":"45 1","pages":"0"},"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":"68 1","pages":""},"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

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

Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS 在Python和R: MIDASpy和rMIDAS中实现多种数据的高效多重输入

2区计算机科学

Journal of Statistical Software Pub Date : 2023-01-01 DOI: 10.18637/jss.v107.i09

Ranjit Lall, Thomas Robinson

引用次数: 2

logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations Logit:基于偏好空间和支付意愿空间效用参数化的多项和混合Logit模型的快速估计

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2022-10-19 DOI: 10.18637/jss.v105.i10

J. Helveston

{"title":"logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations","authors":"J. Helveston","doi":"10.18637/jss.v105.i10","DOIUrl":"https://doi.org/10.18637/jss.v105.i10","url":null,"abstract":"This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with\"preference space\"or\"willingness to pay (WTP) space\"parameterizations, allowing for the direct estimation of marginal WTP. The typical procedure of computing WTP post-estimation using a preference space model can lead to unreasonable distributions of WTP across the population in mixed logit models. The paper provides a discussion of some of the implications of each utility parameterization for WTP estimates. It also highlights some of the design features that enable logitr's performant estimation speed and includes a benchmarking exercise with similar packages. Finally, the paper highlights additional features that are designed specifically for WTP space models, including a consistent user interface for specifying models in either space and a parallelized multi-start optimization loop, which is particularly useful for searching the solution space for different local minima when estimating models with non-convex log-likelihood functions.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"15 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79549247","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: 2