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- The R Journal (December 2018) 10(2); Editor: John Verzani (1)
Articles 61 - 90 of 708
Full-Text Articles in Numerical Analysis and Scientific Computing
Krippendorffsalpha: An R Package For Measuring Agreement Using Krippendorff's Alpha Coefficient, John Hughes
Krippendorffsalpha: An R Package For Measuring Agreement Using Krippendorff's Alpha Coefficient, John Hughes
The R Journal
R package krippendorffsalpha provides tools for measuring agreement using Krippendorff’s α coefficient, a well-known nonparametric measure of agreement (also called inter-rater reliability and various other names). This article first develops Krippendorff’s α in a natural way and situates α among statistical procedures. Then, the usage of package krippendorffsalpha is illustrated via analyses of two datasets, the latter of which was collected during an imaging study of hip cartilage. The package permits users to apply the α methodology using built-in distance functions for the nominal, ordinal, interval, or ratio levels of measurement. User-defined distance functions are also supported. The fitting function …
The R Package Smicd: Statistical Methods For Interval-Censored Data, Paul Walter
The R Package Smicd: Statistical Methods For Interval-Censored Data, Paul Walter
The R Journal
The package allows the use of two new statistical methods for the analysis of intervalcensored data: 1) direct estimation/prediction of statistical indicators and 2) linear (mixed) regression analysis. Direct estimation of statistical indicators, for instance, poverty and inequality indicators, is facilitated by a non parametric kernel density algorithm. The algorithm is able to account for weights in the estimation of statistical indicators. The standard errors of the statistical indicators are estimated with a non parametric bootstrap. Furthermore, the package offers statistical methods for the estimation of linear and linear mixed regression models with an interval-censored dependent variable, particularly random slope …
Finding Optimal Normalizing Transformations Via Bestnormalize, Ryan A. Peterson
Finding Optimal Normalizing Transformations Via Bestnormalize, Ryan A. Peterson
The R Journal
The bestNormalize R package was designed to help users find a transformation that can effectively normalize a vector regardless of its actual distribution. Each of the many normalization techniques that have been developed has its own strengths and weaknesses, and deciding which to use until data are fully observed is difficult or impossible. This package facilitates choosing between a range of possible transformations and will automatically return the best one, i.e., the one that makes data look the most normal. To evaluate and compare the normalization efficacy across a suite of possible transformations, we developed a statistic based on a …
Robustness In Network (Robin): An R Package For Comparison And Validation Of Communities, Valeria Policastro, Dario Righelli, Annamaria Carissimo, Luisa Cutillo, Italia De Feis
Robustness In Network (Robin): An R Package For Comparison And Validation Of Communities, Valeria Policastro, Dario Righelli, Annamaria Carissimo, Luisa Cutillo, Italia De Feis
The R Journal
In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use …
Indexnumber: An R Package For Measuring The Evolution Of Magnitudes, Alejandro Saavedra-Nieves, Paula Saavedra-Nieves
Indexnumber: An R Package For Measuring The Evolution Of Magnitudes, Alejandro Saavedra-Nieves, Paula Saavedra-Nieves
The R Journal
Index numbers are descriptive statistical measures useful in economic settings for comparing simple and complex magnitudes registered, usually in two time periods. Although this theory has a large history, it still plays an important role in modern today’s societies where big amounts of economic data are available and need to be analyzed. After a detailed revision on classical index numbers in literature, this paper is focused on the description of the R package IndexNumber with strong capabilities for calculating them. Two of the four real data sets contained in this library are used for illustrating the determination of the index …
Pdynmc: A Package For Estimating Linear Dynamic Panel Data Models Based On Nonlinear Moment Conditions, Markus Fritsch, Andrew Adrian Yu Pua, Joachim Schnurbus
Pdynmc: A Package For Estimating Linear Dynamic Panel Data Models Based On Nonlinear Moment Conditions, Markus Fritsch, Andrew Adrian Yu Pua, Joachim Schnurbus
The R Journal
This paper introduces pdynmc, an R package that provides users sufficient flexibility and precise control over the estimation and inference in linear dynamic panel data models. The package primarily allows for the inclusion of nonlinear moment conditions and the use of iterated GMM; additionally, visualizations for data structure and estimation results are provided. The current implementation reflects recent developments in literature, uses sensible argument defaults, and aligns commercial and noncommercial estimation commands. Since the understanding of the model assumptions is vital for setting up plausible estimation routines, we provide a broad introduction of linear dynamic panel data models directed towards …
Benchmarking R Packages For Calculation Of Persistent Homology, Eashwar V. Somasundaram, Shael E. Brown, Adam Litzler, Jacob G. Scott, Raoul R. Wadhwa
Benchmarking R Packages For Calculation Of Persistent Homology, Eashwar V. Somasundaram, Shael E. Brown, Adam Litzler, Jacob G. Scott, Raoul R. Wadhwa
The R Journal
Several persistent homology software libraries have been implemented in R. Specifically, the Dionysus, GUDHI, and Ripser libraries have been wrapped by the TDA and TDAstats CRAN packages. These software represent powerful analysis tools that are computationally expensive and, to our knowledge, have not been formally benchmarked. Here, we analyze runtime and memory growth for the 2 R packages and the 3 underlying libraries. We find that datasets with less than 3 dimensions can be evaluated with persistent homology fastest by the GUDHI library in the TDA package. For higher-dimensional datasets, the Ripser library in the TDAstats package is the fastest. …
Unidimensional And Multidimensional Methods For Recurrence Quantification Analysis With Crqa, Moreno I. Coco, Dan Mønster, Giuseppe Leonardi, Rick Dale, Sebastian Wallot
Unidimensional And Multidimensional Methods For Recurrence Quantification Analysis With Crqa, Moreno I. Coco, Dan Mønster, Giuseppe Leonardi, Rick Dale, Sebastian Wallot
The R Journal
Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series and capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version of our crqa 2.0 package. This package …
The Bdpar Package: Big Data Pipelining Architecture For R, Miguel Ferreiro-Díaz, Tomás R. Cotos-Yáñez, José R. Méndez, David Ruano-Ordás
The Bdpar Package: Big Data Pipelining Architecture For R, Miguel Ferreiro-Díaz, Tomás R. Cotos-Yáñez, José R. Méndez, David Ruano-Ordás
The R Journal
In the last years, big data has become a useful paradigm for taking advantage of multiple sources to find relevant knowledge in real domains (such as the design of personalized marketing campaigns or helping to palliate the effects of several fatal diseases). Big data programming tools and methods have evolved over time from a MapReduce to a pipeline-based archetype. Concretely the use of pipelining schemes has become the most reliable way of processing and analyzing large amounts of data. To this end, this work introduces bdpar, a new highly customizable pipeline-based framework (using the OOP paradigm provided by R6 …
Exprior: An R Package For The Formulation Of Ex-Situ Priors, Falk Heße, Karina Cucchi, Nura Kawa, Yoram Rubin
Exprior: An R Package For The Formulation Of Ex-Situ Priors, Falk Heße, Karina Cucchi, Nura Kawa, Yoram Rubin
The R Journal
The exPrior package implements a procedure for formulating informative priors of geostatistical properties for a target field site, called ex-situ priors and introduced in Cucchi et al. (2019). The procedure uses a Bayesian hierarchical model to assimilate multiple types of data coming from multiple sites considered as similar to the target site. This prior summarizes the information contained in the data in the form of a probability density function that can be used to better inform further geostatistical investigations at the site. The formulation of the prior uses ex-situ data, where the data set can either be gathered by the …
Linear Regression With Stationary Errors: The R Package Slm, Emmanuel Caron, Jérôme Dedecker, Bertrand Michel
Linear Regression With Stationary Errors: The R Package Slm, Emmanuel Caron, Jérôme Dedecker, Bertrand Michel
The R Journal
This paper introduces the R package slm, which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear regression in the general context where the error process is strictly stationary with a short memory. We work in the setting of Hannan (1973), who proved the asymptotic normality of the (normalized) least squares estimators (LSE) under very mild conditions on the error process. We propose different ways to estimate the asymptotic covariance matrix of the LSE and then to correct the type I error rates of the usual tests on the parameters (as well as confidence …
A Method For Deriving Information From Running R Code, Mark P. J. Van Der Loo
A Method For Deriving Information From Running R Code, Mark P. J. Van Der Loo
The R Journal
It is often useful to tap information from a running R script. Obvious use cases include monitoring the consumption of resources (time, memory) and logging. Perhaps less obvious cases include tracking changes in R objects or collecting the output of unit tests. In this paper, we demonstrate an approach that abstracts the collection and processing of such secondary information from the running R script. Our approach is based on a combination of three elements. The first element is to build a customized way to evaluate code. The second is labeled local masking and it involves temporarily masking a user-facing function …
Npcure: An R Package For Nonparametric Inference In Mixture Cure Models, Ana López-Cheda, M Amalia Jácome, Ignacio López-De-Ullibarri
Npcure: An R Package For Nonparametric Inference In Mixture Cure Models, Ana López-Cheda, M Amalia Jácome, Ignacio López-De-Ullibarri
The R Journal
Mixture cure models have been widely used to analyze survival data with a cure fraction. They assume that a subgroup of the individuals under study will never experience the event (cured subjects). So, the goal is twofold: to study both the cure probability and the failure time of the uncured individuals through a proper survival function (latency). The R package npcure implements a completely nonparametric approach for estimating these functions in mixture cure models, considering right-censored survival times. Nonparametric estimators for the cure probability and the latency as functions of a covariate are provided. Bootstrap bandwidth selectors for the estimators …
Seedcca: An Integrated R-Package For Canonical Correlation Analysis And Partial Least Squares, Bo-Young Kim, Yunju Im, Jae Keun Yoo
Seedcca: An Integrated R-Package For Canonical Correlation Analysis And Partial Least Squares, Bo-Young Kim, Yunju Im, Jae Keun Yoo
The R Journal
Canonical correlation analysis (CCA) has a long history as an explanatory statistical method in high-dimensional data analysis and has been successfully applied in many scientific fields such as chemometrics, pattern recognition, genomic sequence analysis, and so on. The so-called seedCCA is a newly developed R package that implements not only the standard and seeded CCA but also partial least squares. The package enables us to fit CCA to large-p and small-n data. The paper provides a complete guide. Also, the seeded CCA application results are compared with the regularized CCA in the existing R package. It is believed that the …
Conference Report Of Why R? Turkey 2021, Mustafa Cavus, Olgun Aydin, Ozan Evkaya, Ozancan Ozdemir, Deniz Bezer, Ugur Dar
Conference Report Of Why R? Turkey 2021, Mustafa Cavus, Olgun Aydin, Ozan Evkaya, Ozancan Ozdemir, Deniz Bezer, Ugur Dar
The R Journal
The Why R? Turkey 2021 as a three-day online conference was organized to bring together researchers and professionals from Turkey on April 16-17-18, 2021. We hereby aimed to promote the R community in Turkey by bringing R users with different backgrounds such as genetics, sociology, finance, economy, bio-statistics. There were 8 thematic sessions and 18 invited speakers. In this article, it is aimed to describe the preparation phase, technical details, and the impact of the conference on audience.
News From The Forwards Taskforce, Heather Turner
News From The Forwards Taskforce, Heather Turner
The R Journal
Forwards is an R Foundation taskforce working to widen the participation of underrepresented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the first half of 2021.
Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis
Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis
The R Journal
In the past 6 months, 1290 new packages were added to the CRAN package repository. 116 packages were unarchived and 467 were archived. The following shows the growth of the number of active packages in the CRAN package repository
Regularized Transformation Models: The Tramnet Package, Lucas Kook, Torsten Hothorn
Regularized Transformation Models: The Tramnet Package, Lucas Kook, Torsten Hothorn
The R Journal
The tramnet package implements regularized linear transformation models by combining the flexible class of transformation models from tram with constrained convex optimization implemented in CVXR. Regularized transformation models unify many existing and novel regularized regression models under one theoretical and computational framework. Regularization strategies implemented for transformation models in tramnet include the Lasso, ridge regression, and the elastic net and follow the parameterization in glmnet. Several functionalities for optimizing the hyperparameters, including model-based optimization based on the mlrMBO package, are implemented. A multitude of S3 methods is deployed for visualization, handling, and simulation purposes. This work aims at illustrating all …
The Hbv.Ianigla Hydrological Model, Ezequiel Toum, Mariano H. Masiokas, Ricardo Villalba, Pierre Pitte, Lucas Ruiz
The Hbv.Ianigla Hydrological Model, Ezequiel Toum, Mariano H. Masiokas, Ricardo Villalba, Pierre Pitte, Lucas Ruiz
The R Journal
Over the past 40 years, the HBV (Hydrologiska Byråns Vattenbalansavdelning) hydrological model has been one of the most used worldwide due to its robustness, simplicity, and reliable results. Despite these advantages, the available versions impose some limitations for research studies in mountain watersheds dominated by ice-snow melt runoff (i.e., no glacier module, a limited number of elevation bands, among other constraints). Here we present HBV.IANIGLA, a tool for hydroclimatic studies in regions with steep topography and/or cryospheric processes which provides a modular and extended implementation of the HBV model as an R package. To our knowledge, this is the first …
Clustcurv: An R Package For Determining Groups In Multiple Curves, Nora M. Villanueva, Marta Sestelo, Luis Meira-Machado, Javier Roca-Pardiñas
Clustcurv: An R Package For Determining Groups In Multiple Curves, Nora M. Villanueva, Marta Sestelo, Luis Meira-Machado, Javier Roca-Pardiñas
The R Journal
In many situations, it could be interesting to ascertain whether groups of curves can be performed, especially when confronted with a considerable number of curves. This paper introduces an R package, known as clustcurv, for determining clusters of curves with an automatic selection of their number. The package can be used for determining groups in multiple survival curves as well as for multiple regression curves. Moreover, it can be used with large numbers of curves. An illustration of the use of clustcurv is provided, using both real data examples and artificial data
Towards A Grammar For Processing Clinical Trial Data, Michael J. Kane
Towards A Grammar For Processing Clinical Trial Data, Michael J. Kane
The R Journal
The goal of this paper is to help define a path toward a grammar for processing clinical trials by a) defining a format in which we would like to represent data from standardized clinical trial data b) describing a standard set of operations to transform clinical trial data into this format, and c) to identify a set of verbs and other functionality to facilitate data processing and encourage reproducibility in the processing of these data. It provides a background on standard clinical trial data and goes through a simple preprocessing example illustrating the value of the proposed approach through the …
R Foundation News, Torsten Hothorn
R Foundation News, Torsten Hothorn
The R Journal
Membership fees and donations received between 2021-01-29 and 2021-07-05.
Package Wsbackfit For Smooth Backfitting Estimation Of Generalized Structured Models, Javier Roca-Pardiñas, María Xosé Rodríguez-Álvarez, Stefan Sperlich
Package Wsbackfit For Smooth Backfitting Estimation Of Generalized Structured Models, Javier Roca-Pardiñas, María Xosé Rodríguez-Álvarez, Stefan Sperlich
The R Journal
A package is introduced that provides the weighted smooth backfitting estimator for a large family of popular semiparametric regression models. This family is known as generalized structured models, comprising, for example, generalized varying coefficient model, generalized additive models, mixtures, potentially including parametric parts. The kernel-based weighted smooth backfitting belongs to the statistically most efficient procedures for this model class. Its asymptotic properties are well-understood thanks to the large body of literature about this estimator. The introduced weights allow for the inclusion of sampling weights, trimming, and efficient estimation under heteroscedasticity. Further options facilitate easy handling of aggregated data, prediction, and …
Dchaos: An R Package For Chaotic Time Series Analysis, Julio E. Sandubete, Lorenzo Escot
Dchaos: An R Package For Chaotic Time Series Analysis, Julio E. Sandubete, Lorenzo Escot
The R Journal
Chaos theory has been hailed as a revolution of thoughts and attracting ever-increasing attention of many scientists from diverse disciplines. Chaotic systems are non-linear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory is the detection of chaotic behavior from empirical time-series data. One of the main features of chaos is the well-known initial-value sensitivity property. Methods and techniques related to testing the hypothesis of chaos try to quantify the initial-value sensitive property estimating the so-called Lyapunov exponents. This paper describes the main estimation methods of the Lyapunov exponent from …
Rlumcarlo: Simulating Cold Light Using Monte Carlo Methods, Sebastian Kreutzer, Johannes Friedrich, Vasilis Pagonis, Christian Laag, Ena Rajovic, Christoph Schmidt
Rlumcarlo: Simulating Cold Light Using Monte Carlo Methods, Sebastian Kreutzer, Johannes Friedrich, Vasilis Pagonis, Christian Laag, Ena Rajovic, Christoph Schmidt
The R Journal
The luminescence phenomena of insulators and semiconductors (e.g., natural minerals such as quartz) have various application domains. For instance, Earth Sciences and archaeology exploit luminescence as a dating method. Herein, we present the R package RLumCarlo implementing sets of luminescence models to be simulated with Monte Carlo (MC) methods. MC methods make a powerful ally to all kinds of simulation attempts involving stochastic processes. Luminescence production is such a stochastic process in the form of charge (electron-hole pairs) interaction within insulators and semiconductors. To simulate luminescence-signal curves, we distribute single and independent MC processes to virtual MC clusters. RLumCarlo comes …
Reproducible Summary Tables With The Gtsummary Package, Daniel D. Sjoberg, Karissa Whiting, Michael Curry, Jessica A. Lavery, Joseph Larmarange
Reproducible Summary Tables With The Gtsummary Package, Daniel D. Sjoberg, Karissa Whiting, Michael Curry, Jessica A. Lavery, Joseph Larmarange
The R Journal
The gtsummary package provides an elegant and flexible way to create publication-ready summary tables in R. A critical part of the work of statisticians, data scientists, and analysts is summarizing data sets and regression models in R and publishing or sharing polished summary tables. The gtsummary package was created to streamline these everyday analysis tasks by allowing users to easily create reproducible summaries of data sets, regression models, survey data, and survival data with a simple interface and very little code. The package follows a tidy framework, making it easy to integrate with standard data workflows, and offers many table …
Rocnreg: An R Package For Receiver Operating Characteristic Curve Inference With And Without Covariates, María Xosé Rodríguez-Álvarez, Vanda Inácio
Rocnreg: An R Package For Receiver Operating Characteristic Curve Inference With And Without Covariates, María Xosé Rodríguez-Álvarez, Vanda Inácio
The R Journal
This paper introduces the package ROCnReg that allows estimating the pooled ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden index criterion and the criterion that sets a target value for the false positive …
News From The Bioconductor Project, Bioconductor Core Team
News From The Bioconductor Project, Bioconductor Core Team
The R Journal
Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.13 was released on 20 May, 2021. It is compatible with R 4.1.0 and consists of 2042 software packages, 406 experiment data packages, 965 up-to-date annotation packages, and 29 workflows.
Statistical Quality Control With The Qcr Package, Miguel Flores, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra
Statistical Quality Control With The Qcr Package, Miguel Flores, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra
The R Journal
The R package qcr for Statistical Quality Control (SQC) is introduced and described. It includes a comprehensive set of univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC (qcc, MSQC), qcr provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, [], S, R, p, np, c, u, EWMA, and CUSUM. In addition, it includes functions to perform multivariate control charts such as Hotelling T2 , MEWMA and MCUSUM. As …
Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks, Hong Wang, Ning Li, Shanpeng Li, Gang Li
Jmcmprsk: An R Package For Joint Modelling Of Longitudinal And Survival Data With Competing Risks, Hong Wang, Ning Li, Shanpeng Li, Gang Li
The R Journal
In this paper, we describe an R package named JMcmprsk, for joint modelling of longitudinal and survival data with competing risks. The package in its current version implements two joint models of longitudinal and survival data proposed to handle competing risks survival data together with continuous and ordinal longitudinal outcomes respectively (Elashoff et al., 2008; Li et al., 2010). The corresponding R implementations are further illustrated with real examples. The package also provides simulation functions to simulate datasets for joint modelling with continuous or ordinal outcomes under the competing risks scenario, which provide useful tools to validate and evaluate …