Journal of Statistical Software最新文献_第7页

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

The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories R包森林清查:基于设计的多阶段森林清查全局和小面积估算

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-14 DOI: 10.18637/JSS.V097.I04

Andreas Hill, Alexander Massey, D. Mandallaz

{"title":"The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories","authors":"Andreas Hill, Alexander Massey, D. Mandallaz","doi":"10.18637/JSS.V097.I04","DOIUrl":"https://doi.org/10.18637/JSS.V097.I04","url":null,"abstract":"Forest inventories provide reliable evidence-based information to assess the state and development of forests over time. They typically consist of a random sample of plot locations in the forest that are assessed individually by field crews. Due to the high costs of these terrestrial campaigns, remote sensing information available in high quantity and low costs is frequently incorporated in the estimation process in order to reduce inventory costs or improve estimation precision. With respect to this objective, the application of multiphase forest inventory methods (e.g., double- and triple-sampling regression estimators) has proved to be efficient. While these methods have been successfully applied in practice, the availability of open-source software has been rare if not non-existent. The R package forestinventory provides a comprehensive set of global and small area regression estimators for multiphase forest inventories under simple and cluster sampling. The implemented methods have been demonstrated in various scientific studies ranging from small to large scale forest inventories, and can be used for post-stratification, regression and regression within strata. This article gives an extensive review of the mathematical theory of this family of design-based estimators, puts them into a common framework of forest inventory scenarios and demonstrates their application in the R environment.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"170 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76306607","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: 5

microsynth: Synthetic Control Methods for Disaggregated and Micro-Level Data in R microsynth: R中分解和微观级数据的综合控制方法

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-14 DOI: 10.18637/JSS.V097.I02

Michael W Robbins, Steven Davenport

{"title":"microsynth: Synthetic Control Methods for Disaggregated and Micro-Level Data in R","authors":"Michael W Robbins, Steven Davenport","doi":"10.18637/JSS.V097.I02","DOIUrl":"https://doi.org/10.18637/JSS.V097.I02","url":null,"abstract":"The R package microsynth has been developed for implementation of the synthetic control methodology for comparative case studies involving micro- or meso-level data. The methodology implemented within microsynth is designed to assess the efficacy of a treatment or intervention within a well-defined geographic region that is itself a composite of several smaller regions (where data are available at the more granular level for comparison regions as well). The effect of the intervention on one or more time-varying outcomes is evaluated by determining a synthetic control region that resembles the treatment region across pre-intervention values of the outcome(s) and time-invariant covariates and that is a weighted composite of many untreated comparison regions. The microsynth procedure includes functionality that enables its user to (1) calculate weights for synthetic control, (2) tabulate results for statistical inferences, and (3) create time series plots of outcomes for treatment and synthetic control. In this article, microsynth is described in detail and its application is illustrated using data from a drug market intervention in Seattle, WA.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"10 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79200084","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: 21

Simulating Survival Data Using the simsurv R Package 使用simsurv R包模拟生存数据

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-14 DOI: 10.18637/JSS.V097.I03

S. Brilleman, R. Wolfe, M. Moreno-Betancur, M. Crowther

引用次数: 29