Journal of Statistical Software最新文献_第6页

Optimal Design Generation and Power Evaluation in R: The skpr Package R中的最优设计生成与功率评估:skpr封装

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-08-18 DOI: 10.18637/jss.v099.i01

T. Morgan-Wall, George C. Khoury

引用次数: 5

cold: An R Package for the Analysis of Count Longitudinal Data 用于统计纵向数据分析的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-08-18 DOI: 10.18637/jss.v099.i03

M. H. Gonçalves, M. S. Cabral

引用次数: 1

Dimension Reduction for Time Series in a Blind Source Separation Context Using R 基于R的盲源分离环境下时间序列降维

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-07-12 DOI: 10.18637/jss.v098.i15

K. Nordhausen, M. Matilainen, J. Miettinen, Joni Virta, S. Taskinen

引用次数: 5

SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data. SeqNet：用于生成基因-基因网络和模拟 RNA-Seq 数据的 R 软件包。

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-07-01 Epub Date: 2021-07-10 DOI: 10.18637/jss.v098.i12

Tyler Grimes, Somnath Datta

引用次数: 0

The adoptr Package: Adaptive Optimal Designs for Clinical Trials in R 采用者包:R临床试验的适应性优化设计

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-06-28 DOI: 10.18637/jss.v098.i09

K. Kunzmann, Maximilian Pilz, Carolin Herrmann, G. Rauch, M. Kieser

引用次数: 5

ergm 4: New Features for Analyzing Exponential-Family Random Graph Models ergm 4:分析指数族随机图模型的新特征

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-06-09 DOI: 10.18637/jss.v105.i06

P. Krivitsky, David R. Hunter, M. Morris, Chad Klumb

引用次数: 13

mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect 具有随机效应的总体死亡率和超额死亡率的灵活的基于风险的回归模型拟合

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-06-01 DOI: 10.18637/jss.v098.i14

H. Charvat, A. Belot

{"title":"mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect","authors":"H. Charvat, A. Belot","doi":"10.18637/jss.v098.i14","DOIUrl":"https://doi.org/10.18637/jss.v098.i14","url":null,"abstract":"We present mexhaz, an R package for fitting flexible hazard-based regression models with the possibility to add time-dependent effects of covariates and to account for a two level hierarchical structure in the data through the inclusion of a normally distributed random intercept (i.e., a log-normally distributed shared frailty). Moreover, mexhaz based models can be fitted within the excess hazard setting by allowing the specification of an expected hazard in the model. These models are of common use in the context of the analysis of population-based cancer registry data. Follow-up time can be entered in the right-censored or counting process input style, the latter allowing models with delayed entries. The logarithm of the baseline hazard can be flexibly modeled with B-splines or restricted cubic splines of time. Parameters estimation is based on likelihood maximization: in deriving the contribution of each observation to the cluster-specific conditional likelihood, Gauss-Legendre quadrature is used to calculate the cumulative hazard; the cluster-specific marginal likelihoods are then obtained by integrating over the random effects distribution, using adaptive Gauss-Hermite quadrature. Functions to compute and plot the predicted (excess) hazard and (net) survival (possibly with cluster-specific predictions in the case of random effect models) are provided. We illustrate the use of the different options of the mexhaz package and compare the results obtained with those of other available R packages.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"3 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82399466","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: 10

Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG 基于R包BiDAG的贝叶斯网络结构学习与采样

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-05-02 DOI: 10.18637/jss.v105.i09

Polina Suter, Jack Kuipers, G. Moffa, N. Beerenwinkel

引用次数: 25

BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression BayesSUR:一个用于线性回归中高维多元贝叶斯变量和协方差选择的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-04-28 DOI: 10.18637/jss.v100.i11

Zhi Zhao, Marco Banterle, L. Bottolo, S. Richardson, A. Lewin, M. Zucknick

{"title":"BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression","authors":"Zhi Zhao, Marco Banterle, L. Bottolo, S. Richardson, A. Lewin, M. Zucknick","doi":"10.18637/jss.v100.i11","DOIUrl":"https://doi.org/10.18637/jss.v100.i11","url":null,"abstract":"In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with highdimensional genomic and other omics data, a problem that can be studied with highdimensional multi-response regression, where the response variables are potentially highly correlated. To this purpose, we recently introduced several multivariate Bayesian variable and covariance selection models, e.g., Bayesian estimation methods for sparse seemingly unrelated regression for variable and covariance selection. Several variable selection priors have been implemented in this context, in particular the hotspot detection prior for latent variable inclusion indicators, which results in sparse variable selection for associations between predictors and multiple phenotypes. Here, we also propose an alternative, which uses a Markov random field (MRF) prior for incorporating prior knowledge about the dependence structure of the inclusion indicators. Inference of Bayesian seemingly unrelated regression (SUR) by Markov chain Monte Carlo methods is made computationally feasible by factorisation of the covariance matrix amongst the response variables. In this paper we present BayesSUR, an R package, which allows the user to easily specify and run a range of different Bayesian SUR models, which have been implemented in C++ for computational efficiency. The R package allows the specification of the models in a modular way, where the user chooses the priors for variable selection and for covariance selection separately. We demonstrate the performance of sparse SUR models with the hotspot prior and spike-and-slab MRF prior on synthetic and real data sets representing eQTL or mQTL studies and in vitro anti-cancer drug screening studies as examples for typical applications.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"54 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86877519","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: 8

Statistical Network Analysis with Bergm 统计网络分析与Bergm

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-04-06 DOI: 10.18637/jss.v104.i01

A. Caimo, Lampros Bouranis, Robert W. Krause, N. Friel

{"title":"Statistical Network Analysis with Bergm","authors":"A. Caimo, Lampros Bouranis, Robert W. Krause, N. Friel","doi":"10.18637/jss.v104.i01","DOIUrl":"https://doi.org/10.18637/jss.v104.i01","url":null,"abstract":"Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the complex dependence structure of network data in a wide range of applied contexts. The Bergm package for R has become a popular package to carry out Bayesian parameter inference, missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs. Over the last few years, the package has been considerably improved in terms of efficiency by adopting some of the state-of-the-art Bayesian computational methods for doubly-intractable distributions. Recently, version 5 of the package has been made available on CRAN having undergone a substantial makeover, which has made it more accessible and easy to use for practitioners. New functions include data augmentation procedures based on the approximate exchange algorithm for dealing with missing data, adjusted pseudo-likelihood and pseudo-posterior procedures, which allow for fast approximate inference of the ERGM parameter posterior and model evidence for networks on several thousands nodes.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"74 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78008212","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