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Monotone Regression: A Simple and Fast O(n) PAVA Implementation 单调回归:一种简单快速的O(n) PAVA实现
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v102.c01
F. Busing
{"title":"Monotone Regression: A Simple and Fast O(n) PAVA Implementation","authors":"F. Busing","doi":"10.18637/jss.v102.c01","DOIUrl":"https://doi.org/10.18637/jss.v102.c01","url":null,"abstract":"Efficient coding and improvements in the execution order of the up-and-down-blocks algorithm for monotone or isotonic regression leads to a significant increase in speed as well as a short and simple O ( n ) implementation. Algorithms that use monotone regression as a subroutine, e.g., unimodal or bivariate monotone regression, also benefit from the acceleration. A substantive comparison with and characterization of currently available implementations provides an extensive overview of up-and-down-blocks implementations for the pool-adjacent-violators algorithm for simple linear ordered monotone regression.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"1 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67679178","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
tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student- t Probabilities with Low-Rank Methods in R 用R中的低秩方法计算高维多元正态和学生- t概率
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v101.i04
Jian Cao, M. Genton, D. Keyes, G. Turkiyyah
{"title":"tlrmvnmvt: Computing High-Dimensional Multivariate Normal and Student- t Probabilities with Low-Rank Methods in R","authors":"Jian Cao, M. Genton, D. Keyes, G. Turkiyyah","doi":"10.18637/jss.v101.i04","DOIUrl":"https://doi.org/10.18637/jss.v101.i04","url":null,"abstract":"This paper introduces the usage and performance of the R package tlrmvnmvt, aimed at computing high-dimensional multivariate normal and Student-t probabilities. The package implements the tile-low-rank methods with block reordering and the separationof-variable methods with univariate reordering. The performance is compared with two other state-of-the-art R packages, namely the mvtnorm and the TruncatedNormal packages. Our package has the best scalability and is likely to be the only option in thousands of dimensions. However, for applications with high accuracy requirements, the TruncatedNormal package is more suitable. As an application example, we show that the excursion sets of a latent Gaussian random field can be computed with the tlrmvnmvt package without any model approximation and hence, the accuracy of the produced excursion sets is improved.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"188 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76051787","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
Pro Data Visualization Using R and JavaScript: Analyze and Visualize Key Data on the Web 使用R和JavaScript的Pro数据可视化:分析和可视化Web上的关键数据
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v102.b01
U. Grömping
{"title":"Pro Data Visualization Using R and JavaScript: Analyze and Visualize Key Data on the Web","authors":"U. Grömping","doi":"10.18637/jss.v102.b01","DOIUrl":"https://doi.org/10.18637/jss.v102.b01","url":null,"abstract":"","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"57 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87480454","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
Python and R for the Modern Data Scientist 面向现代数据科学家的Python和R
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.b02
C. Lortie
{"title":"Python and R for the Modern Data Scientist","authors":"C. Lortie","doi":"10.18637/jss.v103.b02","DOIUrl":"https://doi.org/10.18637/jss.v103.b02","url":null,"abstract":"Computation in many fields including those that use statistical software is increasingly driven by needs that can be addressed in many programming ecosystems. In projects that require statistical analyses, both R and Python comprise two frequent resources. In ecology, R is the most frequently used (Lai, Lortie, Muenchen, Yang, and Ma 2019). In bioinformatic gene set analyses, R is also more frequently used in peer-reviewed publications, but Python is still an important statistical resource depending on the specific project (Xie, Jauhari, and Mora 2021). Python outcompetes other languages in use for machine learning and some forms of factor analyses (Hao and Ho 2019; Persson and Khojasteh 2021; Raschka, Patterson, and Nolet 2020). However, the relative frequency that a tool is used for statistical analyses is only one metric of importance and not necessarily a proxy for its merit or its capacity to support innovation and efficient in analyses for practitioners (Zhao, Yan, and Li 2018). It is thus critical that we explore contrasts of at least these two common software languages that support statistics because data scientists can become isolated or polarized within their specific competencies, ideologies, and workflows. A high-level discussion of strengths and weaknesses specific to data endeavors with statistics is germane to both decisions on specific projects and on competency development as a scientist.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"52 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72785050","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
synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data synthACS:空间微模拟建模与综合美国社区调查数据
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v104.i07
Alex P. Whitworth
{"title":"synthACS: Spatial Microsimulation Modeling with Synthetic American Community Survey Data","authors":"Alex P. Whitworth","doi":"10.18637/jss.v104.i07","DOIUrl":"https://doi.org/10.18637/jss.v104.i07","url":null,"abstract":"synthACS is an R package that provides flexible tools for building synthetic micro-datasets based on American Community Survey (ACS) base tables, allows data-extensibility and enables to conduct spatial microsimulation modeling (SMSM) via simulated annealing. To our knowledge, it is the first R package to provide broadly applicable tools for SMSM with ACS data as well as the first SMSM implementation that uses unequal probability sampling in the simulated annealing algorithm. In this paper, we contextualize these developments within the SMSM literature, provide a hands-on user-guide to package synthACS , present a case study of SMSM related to population dynamics, and note areas for future research.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"100 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76993187","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
ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models 广义线性模型的并行化贝叶斯模型平均
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v104.i02
R. Lucchetti, Luca Pedini
{"title":"ParMA: Parallelized Bayesian Model Averaging for Generalized Linear Models","authors":"R. Lucchetti, Luca Pedini","doi":"10.18637/jss.v104.i02","DOIUrl":"https://doi.org/10.18637/jss.v104.i02","url":null,"abstract":"This paper describes the gretl function package ParMA , which provides Bayesian model averaging (BMA) in generalized linear models. In order to overcome the lack of analytical specification for many of the models covered, the package features an implementation of the reversible jump Markov chain Monte Carlo technique, following the original idea by Green (1995), as a flexible tool to model several specifications. Particular attention is devoted to computational aspects such as the automatization of the model building procedure and the parallelization of the sampling scheme.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"104 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87323845","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
rags2ridges: A One-Stop- ℓ2 -Shop for rags2ridge:一站式商店
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v102.i04
Carel F. W. Peeters, A. E. Bilgrau, W. V. van Wieringen
{"title":"rags2ridges: A One-Stop- ℓ2 -Shop for ","authors":"Carel F. W. Peeters, A. E. Bilgrau, W. V. van Wieringen","doi":"10.18637/jss.v102.i04","DOIUrl":"https://doi.org/10.18637/jss.v102.i04","url":null,"abstract":"","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"1 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67679193","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
Doing Meta-Analysis with R - A Hands-On Guide 用R做元分析-实践指南
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v102.b02
C. Lortie
{"title":"Doing Meta-Analysis with R - A Hands-On Guide","authors":"C. Lortie","doi":"10.18637/jss.v102.b02","DOIUrl":"https://doi.org/10.18637/jss.v102.b02","url":null,"abstract":"Scientific synthesis is a diverse field of contemporary science. Syntheses advance knowledge in many domains and can include data compilation, theory syntheses, methods contrasts, and systematic reviews with meta-analyses through an integrated and big-picture view of evidence (Halpern et al. 2020). All these knowledge tools are typically strongly supported by statistical software including the open-source programming language R. Within this environment, there are nearly 100 packages to support meta-analyses each with different functions and specific capabilities (Lortie and Filazzola 2020). Meta-analyses are defined in most domains as the calculation of effect sizes or a weighted relative strength of evidence from a set of studies or trials to then subsequently examine high-level statistical patterns and variance (Gurevitch, Koricheva, Nakagawa, and Stewart 2018). They are increasingly used in many fields of science to examine consilience in hypotheses (Lortie 2014) and have been proposed as the gold or even platinum standard of evidence when there is statistical agreement in the efficacy of an intervention across studies (Stegenga 2011). Consequently, there is a critical need for accessible, pragmatic publications, resources, and texts that enable scientists with varying levels of expertise to engage in scientific syntheses using meta-analysis.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"39 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77664859","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: 622
plot3logit: Ternary Plots for Interpreting Trinomial Regression Models plot3logit:解释三项式回归模型的三元图
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.c01
F. Santi, M. M. Dickson, G. Espa, D. Giuliani
{"title":"plot3logit: Ternary Plots for Interpreting Trinomial Regression Models","authors":"F. Santi, M. M. Dickson, G. Espa, D. Giuliani","doi":"10.18637/jss.v103.c01","DOIUrl":"https://doi.org/10.18637/jss.v103.c01","url":null,"abstract":"This paper presents the R package plot3logit which enables the covariate effects of trinomial regression models to be represented graphically by means of a ternary plot. The aim of the plot is helping the interpretation of regression coefficients in terms of the effects that a change in values of regressors has on the probability distribution of the dependent variable. Such changes may involve either a single regressor, or a group of them (composite changes), and the package permits both cases to be handled in a user-friendly way. Moreover, plot3logit can compute and draw confidence regions of the effects of covariate changes and enables multiple changes and profiles to be represented and compared jointly. Upstream and downstream compatibility makes the package able to work with other R packages or applications other than R .","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"382 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82501290","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
Automatic Identification and Forecasting of Structural Unobserved Components Models with UComp 基于UComp的结构未观测构件模型的自动识别与预测
IF 5.8 2区 计算机科学
Journal of Statistical Software Pub Date : 2022-01-01 DOI: 10.18637/jss.v103.i09
D. J. Pedregal
{"title":"Automatic Identification and Forecasting of Structural Unobserved Components Models with UComp","authors":"D. J. Pedregal","doi":"10.18637/jss.v103.i09","DOIUrl":"https://doi.org/10.18637/jss.v103.i09","url":null,"abstract":"UComp is a powerful library for building unobserved components models, useful for forecasting and other important operations, such us de-trending, cycle analysis, seasonal adjustment, signal extraction, etc. One of the most outstanding features that makes UComp unique among its class of related software implementations is that models may be built automatically by identification algorithms (three versions are available). These algorithms select the best model among many possible combinations. Another relevant feature is that it is coded in C++ , opening the door to link it to different popular and widely used environments, like R , MATLAB , Octave , Python , etc. The implemented models for the components are more general than the usual ones in the field of unobserved components modeling, including different types of trend, cycle, seasonal and irregular components, input variables and outlier detection. The automatic character of the algorithms required the development of many complementary algorithms to control performance and make it applicable to as many different time series as possible. The library is open source and available in different formats in public repositories. The performance of the library is illustrated working on real data in several varied examples.","PeriodicalId":17237,"journal":{"name":"Journal of Statistical Software","volume":"77 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88563192","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
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