Bincor: An R Package For Estimating The Correlation Between Two Unevenly Spaced Time Series,
2019
Basque Centre for Climate Change - BC3, University of the Basque Country
Bincor: An R Package For Estimating The Correlation Between Two Unevenly Spaced Time Series, Josue M. Polanco-Martinez, Martin A. Medina-Elizalde, Maria Fernanda Sanchez Goni, Manfred Mudelsee
The R Journal
This paper presents a computational program named BINCOR (BINned CORrelation) for estimating the correlation between two unevenly spaced time series. This program is also applicable to the situation of two evenly spaced time series not on the same time grid. BINCOR is based on a novel estimation approach proposed by Mudelsee (2010) for estimating the correlation between two climate time series with different timescales. The idea is that autocorrelation (e.g. an AR1 process) means that memory enables values obtained on different time points to be correlated. Binned correlation is performed by resampling the time series under study into time bins …
Changes On Cran,
2019
WU Wirtschaftsuniversität Wien
Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis
The R Journal
In the past 8 months, 1524 new packages were added to the CRAN package repository. 71 packages were unarchived and 302 were archived. The following shows the growth of the number of active packages in the CRAN package repository:
R Foundation News,
2019
Universität Zürich
R Foundation News, Torsten Hothorn
The R Journal
Membership fees and donations received between 2019-01-07 and 2019-09-04
Ciuupi: An R Package For Computing Confidence Intervals That Utilize Uncertain Prior Information,
2019
The University of Melbourne
Ciuupi: An R Package For Computing Confidence Intervals That Utilize Uncertain Prior Information, Mainzer Kabaila, Paul Kabaila
The R Journal
We have created the R package ciuupi to compute confidence intervals that utilize uncertain prior information in linear regression. Unlike post-model-selection confidence intervals, the confidence interval that utilizes uncertain prior information (CIUUPI) implemented in this package has, to an excellent approximation, coverage probability throughout the parameter space that is very close to the desired minimum coverage probability. Furthermore, when the uncertain prior information is correct, the CIUUPI is, on average, shorter than the standard confidence interval constructed using the full linear regression model. In this paper we provide motivating examples of scenarios where the CIUUPI may be used. We then …
R Package For Geometric Shadow Calculations In An Urban Environment,
2019
Ben-Gurion University of the Negev
R Package For Geometric Shadow Calculations In An Urban Environment, Michael Dorman, Evyatar Erell, Adi Vulkan, Itai Kloog
The R Journal
This paper introduces the shadow package for R. The package provides functions for shadow-related calculations in the urban environment, namely shadow height, shadow footprint and Sky View Factor (SVF) calculations, as well as a wrapper function to estimate solar radiation while taking shadow effects into account. All functions operate on a layer of polygons with a height attribute, also known as “extruded polygons” or 2.5D vector data. Such data are associated with accuracy limitations in representing urban environments. However, unlike 3D models, polygonal layers of building outlines along with their height are abundantly available and their processing does not require …
Indoor Positioning And Fingerprinting: The R Package Ipft,
2019
Universitat Jaume I
Indoor Positioning And Fingerprinting: The R Package Ipft, Emilio Sansano, Raúl Montoliu, Óscar Belmonte, Joaquín Torres-Sospedra
The R Journal
Methods based on Received Signal Strength Indicator (RSSI) fingerprinting are in the forefront among several techniques being proposed for indoor positioning. This paper introduces the R package ipft, which provides algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI fingerprint data sets, estimation of positions, comparison of the performance of different positioning models, and graphical visualization of data. Well-known machine learning algorithms are implemented in this package to perform analysis and estimations over RSSI data sets. The paper provides a description of these algorithms and functions, as well as examples of …
Time-Series Clustering In R Using The Dtwclust Package,
2019
University of Nebraska - Lincoln
Time-Series Clustering In R Using The Dtwclust Package, Alexis Sardá-Espinosa
The R Journal
Most clustering strategies have not changed considerably since their initial definition. The common improvements are either related to the distance measure used to assess dissimilarity, or the function used to calculate prototypes. Time-series clustering is no exception, with the Dynamic Time Warping distance being particularly popular in that context. This distance is computationally expensive, so many related optimizations have been developed over the years. Since no single clustering algorithm can be said to perform best on all datasets, different strategies must be tested and compared, so a common infrastructure can be advantageous. In this manuscript, a general overview of shape-based …
Editorial,
2019
R project
Editorial, Norm Matloff
The R Journal
On behalf of the Editorial Board, I am pleased to present Volume 10, Issue 1 of the R Journal. This issue contains 36 contributed articles. The majority of which cover new or newly enhanced packages on CRAN.
Optimization Routines For Enforcing One-To-One Matches In Record Linkage Problems,
2019
Istat - Italian National Institute of Statistics
Optimization Routines For Enforcing One-To-One Matches In Record Linkage Problems, Diego Moretti, Luca Valentino, Tiziana Tuoto
The R Journal
Record linkage aims at quickly and accurately identifying if two records represent the same real world entity. In many applications, we are interested in restricting the linkage results to "1 to 1" links, that is a single record does not appear more than once in the output. This can be dealt with the transport algorithm. The optimization problem, however, grows quadratically in the size of the input, quickly becoming untreatable for cases with a few thousand records. This paper compares different solutions, provided by some R packages for linear programming solvers. The comparison is done in terms of memory usage …
Robustgasp: Robust Gaussian Stochastic Process Emulation In R,
2019
University of California, Santa Barbara
Robustgasp: Robust Gaussian Stochastic Process Emulation In R, Mengyang Gu, Jesus Palomo, James O. Berger
The R Journal
Gaussian stochastic process (GaSP) emulation is a powerful tool for approximating computationally intensive computer models. However, estimation of parameters in the GaSP emulator is a challenging task. No closed-form estimator is available and many numerical problems arise with standard estimates, e.g., the maximum likelihood estimator. In this package, we implement a marginal posterior mode estimator, for special priors and parameterizations. This estimation method that meets the robust parameter estimation criteria was discussed in Gu et al. (2018); mathematical reasons are provided therein to explain why robust parameter estimation can greatly improve predictive performance of the emulator. In addition, inert inputs …
Mixedsde: A Package To Fit Mixed Stochastic Differential Equations,
2019
Université Grenoble Alpes, Université Paris Descartes 45, Université Pierre et Marie Curie 4
Mixedsde: A Package To Fit Mixed Stochastic Differential Equations, Charlotte Dion, Simone Hermann, Adeline Samson
The R Journal
Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyze neuronal data, crack length data, pharmacokinetics, financial data, to cite some applications among other. The R package focuses on the estimation of SDEs with linear random effects in the drift. The goal is to estimate the common density of the random effects from repeated discrete observations of the SDE. The package mixedsde proposes three estimation methods: a Bayesian parametric, …
Atable: Create Tables For Clinical Trial Reports,
2019
German Center for Neurodegenerative Diseases (DZNE)
Atable: Create Tables For Clinical Trial Reports, Armin Ströbel
The R Journal
Examining distributions of variables is the first step in the analysis of a clinical trial before more specific modelling can begin. Reporting these results to stakeholders of the trial is an essential part of a statistician’s work. The atable package facilitates these steps by offering easy-to-use but still flexible functions.
Fclust: An R Package For Fuzzy Clustering,
2019
Sapienza University of Rome
Fclust: An R Package For Fuzzy Clustering, Maria Brigida Ferraro, Paolo Giordani, Alessio Serafini
The R Journal
Fuzzy clustering methods discover fuzzy partitions where observations can be softly assigned to more than one cluster. The package fclust is a toolbox for fuzzy clustering in the R programming language. It not only implements the widely used fuzzy k-means (FkM) algorithm, but also many FkM variants. Fuzzy cluster similarity measures, cluster validity indices and cluster visualization tools are also offered. In the current version, all the functions are rewritten in the C++ language allowing their application in large-size problems. Moreover, new fuzzy relational clustering algorithms for partitioning qualitative/mixed data are provided together with an improved version of the so-called …
Mdfs: Multidimensional Feature Selection In R,
2019
University of Bialystok
Mdfs: Multidimensional Feature Selection In R, Radosław Piliszek, Krzysztof Mnich, Szymon Migacz, Paweł Tabaszewski, Andrzej Sułecki, Aneta Polewko-Klim, Witold Rudnicki
The R Journal
Identification of informative variables in an information system is often performed using simple one-dimensional filtering procedures that discard information about interactions between variables. Such an approach may result in removing some relevant variables from consideration. Here we present an R package MDFS (MultiDimensional Feature Selection) that performs identification of informative variables taking into account synergistic interactions between multiple descriptors and the decision variable. MDFS is an implementation of an algorithm based on information theory (Mnich and Rudnicki, 2017). The computational kernel of the package is implemented in C++. A high-performance version implemented in CUDA C is also available. The application …
Connecting R With D3 For Dynamic Graphics, To Explore Multivariate Data With Tours,
2019
Monash University
Connecting R With D3 For Dynamic Graphics, To Explore Multivariate Data With Tours, Michael Kipp, Ursula Laa, Dianne Cook
The R Journal
The tourr package in R has several algorithms and displays for showing multivariate data as a sequence of low-dimensional projections. It can display as a movie but has no capacity for interaction, such as stop/go, change tour type, drop/add variables. The tourrGui package provides these sorts of controls, but the interface is programmed with the dated RGtk2 package. This work explores using custom messages to pass data from R to D3 for viewing, using the Shiny framework. This is an approach that can be generally used for creating all sorts of interactive graphics.
Matching With Clustered Data: The Cmatching Package In R,
2019
University of Cagliari
Matching With Clustered Data: The Cmatching Package In R, Massimo Cannas, Bruno Arpino
The R Journal
Matching is a well known technique to balance covariates distribution between treated and control units in non-experimental studies. In many fields, clustered data are a very common occurrence in the analysis of observational data and the clustering can add potentially interesting information. Matching algorithms should be adapted to properly exploit the hierarchical structure. In this article we present the CMatching package implementing matching algorithms for clustered data. The package provides functions for obtaining a matched dataset along with estimates of most common parameters of interest and model-based standard errors. A propensity score matching analysis, relating math proficiency with homework completion …
Identifying And Testing Recursive Vs. Interdependent Links In Simultaneous Equation Models Via The Sire Package,
2019
Department of Economic Policy Largo Gemelli
Identifying And Testing Recursive Vs. Interdependent Links In Simultaneous Equation Models Via The Sire Package, Gianmarco Vacca, Maria Grazia Zoia
The R Journal
Simultaneous equation models (SEMs) are composed of relations which either represent unidirectional links, which entail a causal interpretation, or bidirectional links, due to feedback loops, which lead to the notion of interdependence. The issue is of prominent interest in several respects. Investigating the causal structure of a SEM, on the one hand, brings to light the theoretical assumptions behind the model and, on the other hand, pilots the choice of the befitting estimation method and of which policy to implement. This paper provides an operational method to distinguish causal relations from interdependent ones in SEMs, such as macro-econometric models, models …
Rssampling: A Pioneering Package For Ranked Set Sampling,
2019
Dokuz Eylul University
Rssampling: A Pioneering Package For Ranked Set Sampling, Busra Sevinc, Bekir Cetintav, Melek Esemen, Selma Gurler
The R Journal
Ranked set sampling (RSS) is an advanced data collection method when the exact measurement of an observation is difficult and/or expensive used in a number of research areas, e.g., environment, bioinformatics, ecology, etc. In this method, random sets are drawn from a population and the units in sets are ranked with a ranking mechanism which is based on a visual inspection or a concomitant variable. Because of the importance of working with a good design and easy analysis, there is a need for a software tool which provides sampling designs and statistical inferences based on RSS and its modifications. This …
Title: Ipwerrory: An R Package For Estimation Of Average Treatment Effect With Misclassified Binary Outcome,
2019
University of Waterloo
Title: Ipwerrory: An R Package For Estimation Of Average Treatment Effect With Misclassified Binary Outcome, Di Shu, Grace Y. Yi
The R Journal
It has been well documented that ignoring measurement error may result in severely biased inference results. In recent years, there has been limited but increasing research on causal inference with measurement error. In the presence of misclassified binary outcome variable, Shu and Yi (2017) considered the inverse probability weighted estimation of the average treatment effect and proposed valid estimation methods to correct for misclassification effects for various settings. To expedite the application of those methods for situations where misclassification in the binary outcome variable is a real concern, we implement correction methods proposed by Shu and Yi (2017) and develop …
Semicomprisks: An R Package For The Analysis Of Independent And Cluster-Correlated Semi-Competing Risks Data,
2019
Pontificia Universidad Católica de Chile
Semicomprisks: An R Package For The Analysis Of Independent And Cluster-Correlated Semi-Competing Risks Data, Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee
The R Journal
Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for …
