Publisher: American Statistical Association   (Total: 5 journals)   [Sort alphabetically]

Showing 1 - 5 of 5 Journals sorted by number of followers
J. of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
J. of Business & Economic Statistics     Full-text available via subscription   (Followers: 40, SJR: 3.664, CiteScore: 2)
J. of Statistical Software     Open Access   (Followers: 21, SJR: 13.802, CiteScore: 16)
Statistics in Biopharmaceutical Research     Full-text available via subscription   (Followers: 16, SJR: 1.187, CiteScore: 1)
Technometrics     Full-text available via subscription   (Followers: 9, SJR: 1.546, CiteScore: 2)
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Journal of Statistical Software
Journal Prestige (SJR): 13.802
Citation Impact (citeScore): 16
Number of Followers: 21  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1548-7660 - ISSN (Online) 1548-7660
Published by American Statistical Association Homepage  [5 journals]
  • subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical

    • Authors: Nicolas M. Ballarini; Marius Thomas, Gerd K. Rosenkranz, Björn Bornkamp
      Abstract: The investigation of subgroups is an integral part of randomized clinical trials. Exploration of treatment effect heterogeneity is typically performed by covariate-adjusted analyses including treatment-by-covariate interactions. Several statistical techniques, such as model averaging and bagging, were proposed recently to address the problem of selection bias in treatment effect estimates for subgroups. In this paper, we describe the subtee R package for subgroup treatment effect estimation. The package can be used for all commonly encountered type of outcomes in clinical trials (continuous, binary, survival, count). We also provide additional functions to build the subgroup variables to be used and to plot the results using forest plots. The functions are demonstrated using data from a clinical trial investigating a treatment for prostate cancer with a survival endpoint.
      PubDate: Mon, 25 Oct 2021 00:00:00 +000
  • TRES: An R Package for Tensor Regression and Envelope Algorithms

    • Authors: Jing Zeng; Wenjing Wang, Xin Zhang
      Abstract: Recently, there has been a growing interest in tensor data analysis, where tensor regression is the cornerstone of statistical modeling for tensor data. The R package TRES provides the standard least squares estimators and the more efficient envelope estimators for the tensor response regression (TRR) and the tensor predictor regression (TPR) models. Envelope methodology provides a relatively new class of dimension reduction techniques that jointly models the regression mean and covariance parameters. Three types of widely applicable envelope estimation algorithms are implemented and applied to both TRR and TPR models.
      PubDate: Fri, 22 Oct 2021 00:00:00 +000
  • Flexible Scan Statistics for Detecting Spatial Disease Clusters: The
           rflexscan R Package

    • Authors: Takahiro Otani; Kunihiko Takahashi
      Abstract: The spatial scan statistic is commonly used to detect spatial disease clusters in epidemiological studies. Among the various types of scan statistics, the flexible scan statistic proposed by Tango and Takahashi (2005) is one of the most promising methods to detect arbitrarily-shaped clusters. In this paper, we introduce a new R package, rflexscan (Otani and Takahashi 2021), that provides efficient and easy-to-use methods for analyses of spatial count data using the flexible spatial scan statistic. The package is designed for any of the following interrelated purposes: to evaluate whether reported spatial disease clusters are statistically significant, to test whether a disease is randomly distributed over space, and to perform geographical surveillance of disease to detect areas of significantly high rates. The functionality of the package is demonstrated through an application to a public-domain small-area cancer incidence dataset in New York State, USA, between 2005 and 2009.
      PubDate: Fri, 22 Oct 2021 00:00:00 +000
  • Permutation Tests for Regression, ANOVA, and Comparison of Signals: The
           permuco Package

    • Authors: Jaromil Frossard; Olivier Renaud
      Abstract: Recent methodological researches produced permutation methods to test parameters in presence of nuisance variables in linear models or repeated measures ANOVA. Permutation tests are also particularly useful to overcome the multiple comparisons problem as they are used to test the effect of factors or variables on signals while controlling the family-wise error rate (FWER). This article introduces the permuco package which implements several permutation methods. They can all be used jointly with multiple comparisons procedures like the cluster-mass tests or threshold-free cluster enhancement (TFCE). The permuco package is designed, first, for univariate permutation tests with nuisance variables, like regression and ANOVA; and secondly, for comparing signals as required, for example, for the analysis of event-related potential (ERP) of experiments using electroencephalography (EEG). This article describes the permutation methods and the multiple comparisons procedures implemented. A tutorial for each of theses cases is provided.
      PubDate: Fri, 22 Oct 2021 00:00:00 +000
  • BAMBI: An R Package for Fitting Bivariate Angular Mixture Models

    • Authors: Saptarshi Chakraborty; Samuel W. K. Wong
      Abstract: Statistical analyses of directional or angular data have applications in a variety of fields, such as geology, meteorology and bioinformatics. There is substantial literature on descriptive and inferential techniques for univariate angular data, with the bivariate (or more generally, multivariate) cases receiving more attention in recent years. More specifically, the bivariate wrapped normal, von Mises sine and von Mises cosine distributions, and mixtures thereof, have been proposed for practical use. However, there is a lack of software implementing these distributions and the associated inferential techniques. In this article, we introduce BAMBI, an R package for analyzing bivariate (and univariate) angular data. We implement random data generation, density evaluation, and computation of theoretical summary measures (variances and correlation coefficients) for the three aforementioned bivariate angular distributions, as well as two univariate angular distributions: the univariate wrapped normal and the univariate von Mises distribution. The major contribution of BAMBI to statistical computing is in providing Bayesian methods for modeling angular data using finite mixtures of these distributions. We also provide functions for visual and numerical diagnostics and Bayesian inference for the fitted models. In this article, we first provide a brief review of the distributions and techniques used in BAMBI, then describe the capabilities of the package, and finally conclude with demonstrations of mixture model fitting using BAMBI on the two real data sets included in the package, one univariate and one bivariate.
      PubDate: Mon, 18 Oct 2021 00:00:00 +000
  • IncDTW: An R Package for Incremental Calculation of Dynamic Time Warping

    • Authors: Maximilian Leodolter; Claudia Plant, Norbert Brändle
      Abstract: Dynamic time warping (DTW) is a popular distance measure for time series analysis and has been applied in many research domains. This paper proposes the R package IncDTW for the incremental calculation of DTW, and based on this principle IncDTW also helps to classify or cluster time series, or perform subsequence matching and k-nearest neighbor search. DTW can measure dissimilarity between two temporal sequences which may vary in speed, with a major downside of high computational costs. Especially for analyzing live data streams, subsequence matching or calculating pairwise distance matrices, runtime intensive computations are unfavorable or can even make the analysis intractable. IncDTW tackles this problem by a vector-based implementation of the DTW algorithm to reduce the space complexity from a quadratic to a linear level in number of observations, and an incremental calculation of DTW for updating interim results to reduce the runtime complexity for online applications. We discuss the fundamental functionalities of IncDTW and apply the package to classify multivariate live stream accelerometer time series for activity recognition. Finally, comparative runtime experiments with various R and Python packages for various data analysis tasks emphasize the broad applicability of IncDTW.
      PubDate: Fri, 24 Sep 2021 00:00:00 +000
  • D-STEM v2: A Software for Modeling Functional Spatio-Temporal Data

    • Authors: Yaqiong Wang; Francesco Finazzi, Alessandro Fassò
      Abstract: Functional spatio-temporal data naturally arise in many environmental and climate applications where data are collected in a three-dimensional space over time. The MATLAB D-STEM v1 software package was first introduced for modeling multivariate space-time data and has been recently extended to D-STEM v2 to handle functional data indexed across space and over time. This paper introduces the new modeling capabilities of DSTEM v2 as well as the complexity reduction techniques required when dealing with large data sets. Model estimation, validation and dynamic kriging are demonstrated in two case studies, one related to ground-level air quality data in Beijing, China, and the other one related to atmospheric profile data collected globally through radio sounding.
      PubDate: Fri, 24 Sep 2021 00:00:00 +000
  • Conditional Model Selection in Mixed-Effects Models with cAIC4

    • Authors: Benjamin Säfken; David Rügamer, Thomas Kneib, Sonja Greven
      Abstract: Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this paper we introduce the R package cAIC4 that allows for the computation of the conditional Akaike information criterion (cAIC). Computation of the conditional AIC needs to take into account the uncertainty of the random effects variance and is therefore not straightforward. We introduce a fast and stable implementation for the calculation of the cAIC for (generalized) linear mixed models estimated with lme4 and (generalized) additive mixed models estimated with gamm4. Furthermore, cAIC4 offers a stepwise function that allows for an automated stepwise selection scheme for mixed models based on the cAIC. Examples of many possible applications are presented to illustrate the practical impact and easy handling of the package.
      PubDate: Wed, 22 Sep 2021 00:00:00 +000
  • Regularized Ordinal Regression and the ordinalNet R Package

    • Authors: Michael J. Wurm; Paul J. Rathouz, Bret M. Hanlon
      Abstract: Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, that can be used to model either ordered or unordered categorical response data. We call this the elementwise link multinomial-ordinal class, and it includes widely used models such as multinomial logistic regression (which also has an ordinal form) and ordinal logistic regression (which also has an unordered multinomial form). We introduce an elastic net penalty class that applies to either model form, and additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.
      PubDate: Wed, 08 Sep 2021 00:00:00 +000
  • dynamichazard: Dynamic Hazard Models Using State Space Models

    • Authors: Benjamin Christoffersen
      Abstract: The dynamichazard package implements state space models that can provide a computationally efficient way to model time-varying parameters in survival analysis. I cover the models and some of the estimation methods implemented in dynamichazard, apply them to a large data set, and perform a simulation study to illustrate the methods' computation time and performance. One of the methods is compared with other models implemented in R which allow for left-truncation, right-censoring, time-varying covariates, and timevarying parameters.
      PubDate: Wed, 08 Sep 2021 00:00:00 +000
  • Robust Analysis of Sample Selection Models through the R Package ssmrob

    • Authors: Mikhail Zhelonkin; Elvezio Ronchetti
      Abstract: The aim of this paper is to describe the implementation and to provide a tutorial for the R package ssmrob, which is developed for robust estimation and inference in sample selection and endogenous treatment models. The sample selectivity issue occurs in practice in various fields, when a non-random sample of a population is observed, i.e., when observations are present according to some selection rule. It is well known that the classical estimators introduced by Heckman (1979) are very sensitive to small deviations from the distributional assumptions (typically the normality assumption on the error terms). Zhelonkin, Genton, and Ronchetti (2016) investigated the robustness properties of these estimators and proposed robust alternatives to the estimator and the corresponding test. We briefly discuss the robust approach and demonstrate its performance in practice by providing several empirical examples. The package can be used both to produce a complete robust statistical analysis of these models which complements the classical one and as a set of useful tools for exploratory data analysis. Specifically, robust estimators and standard errors of the coefficients of both the selection and the regression equations are provided together with a robust test of selectivity. The package therefore provides additional useful information to practitioners in different fields of applications by enhancing their statistical analysis of these models.
      PubDate: Sat, 21 Aug 2021 00:00:00 +000
  • Causal Effect Identification from Multiple Incomplete Data Sources: A
           General Search-Based Approach

    • Authors: Santtu Tikka; Antti Hyttinen, Juha Karvanen
      Abstract: Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature such as combined transportability and selection bias, or multiple sources of selection bias. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to the generality of do-calculus, the search is capable of taking more advanced datagenerating mechanisms into account along with an arbitrary type of both observational and experimental source distributions. The search is enhanced via a heuristic and search space reduction techniques. The approach, called do-search, is provably sound, and it is complete with respect to identifiability problems that have been shown to be completely characterized by do-calculus. When extended with additional rules, the search is capable of handling missing data problems as well. With the versatile search, we are able to approach new problems for which no other algorithmic solutions exist. We perform a systematic analysis of bivariate missing data problems and study causal inference under case-control design. We also present the R package dosearch that provides an interface for a C++ implementation of the search.
      PubDate: Sat, 21 Aug 2021 00:00:00 +000
  • Optimal Design Generation and Power Evaluation in R: The skpr Package

    • Authors: Tyler Morgan-Wall; George Khoury
      Abstract: The R package skpr provides a suite of functions to generate and evaluate experimental designs. Package skpr generates D, I, Alias, A, E, T, and G-optimal designs, and supports custom user-defined optimality criteria, N-level split-plot designs, mixture designs, and design augmentation. Also included are a collection of analytic and Monte Carlo power evaluation functions for normal, non-normal, random effects, and survival models, as well as tools to plot fraction of design space plots and correlation maps. Additionally, skpr includes a flexible framework for the user to perform custom power analyses with external libraries and user-defined functions, as well as a graphical user interface that wraps most of the functionality of the package in a point-and-click web application.
      PubDate: Wed, 18 Aug 2021 12:38:17 +000
  • The R Package sentometrics to Compute, Aggregate, and Predict with Textual

    • Authors: David Ardia; Keven Bluteau, Samuel Borms, Kris Boudt
      Abstract: We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.
      PubDate: Wed, 18 Aug 2021 12:38:17 +000
  • cold: An R Package for the Analysis of Count Longitudinal Data

    • Authors: M. Helena Gonçalves; M. Salomé Cabral
      Abstract: This paper describes the R package cold for the analysis of count longitudinal data. In this package marginal and random effects models are considered. In both cases estimation is via maximization of the exact likelihood and serial dependence among observations is assumed to be of Markovian type and referred as the integer-valued autoregressive of order one process. For random effects models adaptive Gaussian quadrature and Monte Carlo methods are used to compute integrals whose dimension depends on the structure of random effects. cold is written partly in R language, partly in Fortran 77, interfaced through R and is built following the S4 formulation of R methods.
      PubDate: Wed, 18 Aug 2021 12:38:17 +000
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