Journal of Statistical Software最新文献_第8页

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

A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R 几种二元不相似矩阵基于模型聚类的贝叶斯方法:R中的dmbc包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v100.i16

S. Venturini, R. Piccarreta

{"title":"A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R","authors":"S. Venturini, R. Piccarreta","doi":"10.18637/jss.v100.i16","DOIUrl":"https://doi.org/10.18637/jss.v100.i16","url":null,"abstract":"We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we consider the case when S matrices are available, each describing the dissimilarities among the same n objects, possibly expressed by S subjects (judges), or measured under different experimental conditions, or with reference to different characteristics of the objects themselves. In particular, we focus on binary dissimilarities, taking values 0 or 1 depending on whether or not two objects are deemed as dissimilar. We are interested in analyzing such data using multidimensional scaling (MDS). Differently from standard MDS algorithms, our goal is to cluster the dissimilarity matrices and, simultaneously, to extract an MDS configuration specific for each cluster. To this end, we develop a fully Bayesian three-way MDS approach, where the elements of each dissimilarity matrix are modeled as a mixture of Bernoulli random vectors. The parameter estimates and the MDS configurations are derived using a hybrid Metropolis-Gibbs Markov Chain Monte Carlo algorithm. We also propose a BIC-like criterion for jointly selecting the optimal number of clusters and latent space dimensions. We illustrate our approach referring both to synthetic data and to a publicly available data set taken from the literature. For the sake of efficiency, the core computations in the package are implemented in C/C++. The package also allows the simulation of multiple chains through the support of the parallel package.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"32 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76029407","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

Data Science in Stata 16: Frames, Lasso, and Python Integration Stata中的数据科学16:框架、套索和Python集成

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v098.s01

A. T. Ho, Kim P. Huynh, David T. Jacho-Chávez, Diego Rojas-Baez

{"title":"Data Science in Stata 16: Frames, Lasso, and Python Integration","authors":"A. T. Ho, Kim P. Huynh, David T. Jacho-Chávez, Diego Rojas-Baez","doi":"10.18637/jss.v098.s01","DOIUrl":"https://doi.org/10.18637/jss.v098.s01","url":null,"abstract":"Stata (StataCorp 2019) is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata’s recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release, Stata has implemented several changes and additions (see https://www.stata.com/new-in-stata/) that include lasso, multiple data sets in memory, meta-analysis, choice models, Python integration, Bayes-multiple chains, panel-data extended regression models, sample-size analysis for confidence intervals, panel-data mixed logit, nonlinear dynamic stochastic general equilibrium (DSGE) models, numerical integration. This review covers the most salient innovations in Stata 16. It is the first release that brings along an implementation of machine-learning tools. The three innovations we consider in this review are: (1) Multiple data sets in Memory, (2) Lasso for causal inference, and (3) Python integration. The following three sections are used to describe each one of these innovations. The last section are the final thoughts and conclusions of our review.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"3 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80646023","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: 6

subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials 临床试验中亚组治疗效果评估的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v099.i14

Nicolás M Ballarini, Marius Thomas, G. Rosenkranz, B. Bornkamp

引用次数: 5

IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping IncDTW:一个动态时间翘曲增量计算的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v099.i09

Maximilian Leodolter, C. Plant, Norbert Brändle

引用次数: 5

dynamichazard: Dynamic Hazard Models Using State Space Models 动态危害:使用状态空间模型的动态危害模型

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v099.i07

Benjamin Christoffersen

引用次数: 4

BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures 一个基于Pitman-Yor混合物的贝叶斯非参数建模的R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v100.i15

R. Corradin, A. Canale, Bernardo Nipoti

引用次数: 12

Flexible Scan Statistics for Detecting Spatial Disease Clusters: The rflexscan R Package 用于检测空间疾病簇的灵活扫描统计:rflexscan R包

IF 5.8 2区计算机科学

Journal of Statistical Software Pub Date : 2021-01-01 DOI: 10.18637/jss.v099.i13

Takahiro Otani, Kunihiko Takahashi

引用次数: 7