Journal of Statistical Software最新文献_第7页

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

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression 深度回归:半结构化深度分布回归的灵活神经网络框架

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-04-06 DOI: 10.18637/jss.v105.i02

D. Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, N. Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, B. Bischl, C. Müller

{"title":"deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression","authors":"D. Rügamer, Ruolin Shen, Christina Bukas, Lisa Barros de Andrade e Sousa, Dominik Thalmeier, N. Klein, Chris Kolb, Florian Pfisterer, Philipp Kopper, B. Bischl, C. Müller","doi":"10.18637/jss.v105.i02","DOIUrl":"https://doi.org/10.18637/jss.v105.i02","url":null,"abstract":"In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encompasses (1) a modular neural network building system based on the deep learning library pkg{TensorFlow} for the fusion of various statistical and deep learning approaches, (2) an orthogonalization cell to allow for an interpretable combination of different subnetworks, as well as (3) pre-processing steps necessary to set up such models. The software package allows to define models in a user-friendly manner via a formula interface that is inspired by classical statistical model frameworks such as pkg{mgcv}. The packages' modular design and functionality provides a unique resource for both scalable estimation of complex statistical models and the combination of approaches from deep learning and statistics. This allows for state-of-the-art predictive performance while simultaneously retaining the indispensable interpretability of classical statistical models.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"327 3","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72435953","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: 15

mosum: A Package for Moving Sums in Change-Point Analysis mosum:一个在变化点分析中移动总数的包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-03-19 DOI: 10.18637/JSS.V097.I08

Alexander Meier, C. Kirch, Haeran Cho

引用次数: 32

svars: An R Package for Data-Driven Identification in Multivariate Time Series Analysis svars:一个用于多变量时间序列分析中数据驱动识别的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-03-19 DOI: 10.18637/JSS.V097.I05

Alexander Lange, B. Dalheimer, H. Herwartz, Simone Maxand

引用次数: 16

FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs. FamEvent：用于在家庭设计中生成时间到事件数据并对其进行建模的 R 软件包。

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-03-01 Epub Date: 2021-03-19 DOI: 10.18637/jss.v097.i07

Yun-Hee Choi, Laurent Briollais, Wenqing He, Karen Kopciuk

{"title":"FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs.","authors":"Yun-Hee Choi, Laurent Briollais, Wenqing He, Karen Kopciuk","doi":"10.18637/jss.v097.i07","DOIUrl":"10.18637/jss.v097.i07","url":null,"abstract":"FamEvent is a comprehensive R package for simulating and modelling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an Expectation-Maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"97 7","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427460/pdf/nihms-1735562.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39408263","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

intRinsic: An R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset 一个基于模型估计数据集内在维数的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-02-23 DOI: 10.18637/jss.v106.i09

Francesco Denti

{"title":"intRinsic: An R Package for Model-Based Estimation of the Intrinsic Dimension of a Dataset","authors":"Francesco Denti","doi":"10.18637/jss.v106.i09","DOIUrl":"https://doi.org/10.18637/jss.v106.i09","url":null,"abstract":"This article illustrates intRinsic, an R package that implements novel state-of-the-art likelihood-based estimators of the intrinsic dimension of a dataset, an essential quantity for most dimensionality reduction techniques. In order to make these novel estimators easily accessible, the package contains a small number of high-level functions that rely on a broader set of efficient, low-level routines. Generally speaking, intRinsic encompasses models that fall into two categories: homogeneous and heterogeneous intrinsic dimension estimators. The first category contains the two nearest neighbors estimator, a method derived from the distributional properties of the ratios of the distances between each data point and its first two closest neighbors. The functions dedicated to this method carry out inference under both the frequentist and Bayesian frameworks. In the second category, we find the heterogeneous intrinsic dimension algorithm, a Bayesian mixture model for which an efficient Gibbs sampler is implemented. After presenting the theoretical background, we demonstrate the performance of the models on simulated datasets. This way, we can facilitate the exposition by immediately assessing the validity of the results. Then, we employ the package to study the intrinsic dimension of the Alon dataset, obtained from a famous microarray experiment. Finally, we show how the estimation of homogeneous and heterogeneous intrinsic dimensions allows us to gain valuable insights into the topological structure of a dataset.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"14 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85981197","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: 4

sensobol: An R Package to Compute Variance-Based Sensitivity Indices sensobol:一个计算基于方差的灵敏度指数的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-22 DOI: 10.18637/jss.v102.i05

A. Puy, S. L. Piano, Andrea Saltelli, S. Levin

引用次数: 30

Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package 非参数机器学习和贝叶斯加性回归树的高效计算:BART R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-14 DOI: 10.18637/JSS.V097.I01

R. Sparapani, Charles Spanbauer, R. McCulloch

{"title":"Nonparametric Machine Learning and Efficient Computation with Bayesian Additive Regression Trees: The BART R Package","authors":"R. Sparapani, Charles Spanbauer, R. McCulloch","doi":"10.18637/JSS.V097.I01","DOIUrl":"https://doi.org/10.18637/JSS.V097.I01","url":null,"abstract":"In this article, we introduce the BART R package which is an acronym for Bayesian additive regression trees. BART is a Bayesian nonparametric, machine learning, ensemble predictive modeling method for continuous, binary, categorical and time-to-event outcomes. Furthermore, BART is a tree-based, black-box method which fits the outcome to an arbitrary random function, f , of the covariates. The BART technique is relatively computationally efficient as compared to its competitors, but large sample sizes can be demanding. Therefore, the BART package includes efficient state-of-the-art implementations for continuous, binary, categorical and time-to-event outcomes that can take advantage of modern off-the-shelf hardware and software multi-threading technology. The BART package is written in C++ for both programmer and execution efficiency. The BART package takes advantage of multi-threading via forking as provided by the parallel package and OpenMP when available and supported by the platform. The ensemble of binary trees produced by a BART fit can be stored and re-used later via the R predict function. In addition to being an R package, the installed BART routines can be called directly from C++. The BART package provides the tools for your BART toolbox.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"115 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86293135","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: 72