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
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 …
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 …
R News,
2019
University of Nebraska - Lincoln
Nowcasting: An R Package For Predicting Economic Variables Using Dynamic Factor Models,
2019
EPGE Brazilian School of Economics and Finance
Nowcasting: An R Package For Predicting Economic Variables Using Dynamic Factor Models, Serge De Valk, Daiane De Mattos, Pedro Ferreira
The R Journal
The nowcasting package provides the tools to make forecasts of monthly or quarterly economic variables using dynamic factor models. The objective is to help the user at each step of the forecasting process, starting with the construction of a database, all the way to the interpretation of the forecasts. The dynamic factor model adopted in this package is based on the articles from Giannone et al. (2008) and Banbura et al. (2011). Although there exist several other dynamic factor model packages available for R, ours provides an environment to easily forecast economic variables and interpret results.
Optimparallel: An R Package Providing A Parallel Version Of The L-Bfgs-B Optimization Method,
2019
Colorado School of Mines
Optimparallel: An R Package Providing A Parallel Version Of The L-Bfgs-B Optimization Method, Florian Gerber, Reinhard Furrer
The R Journal
The R package optimParallel provides a parallel version of the L-BFGS-B optimization method of optim(). The main function of the package is optimParallel(), which has the same usage and output as optim(). Using optimParallel() can significantly reduce the optimization time, especially when the evaluation time of the objective function is large and no analytical gradient is available. We introduce the R package and illustrate its implementation, which takes advantage of the lexical scoping mechanism of R.
Integration Of Networks And Pathways With Starbiotrek Package,
2019
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR)
Integration Of Networks And Pathways With Starbiotrek Package, Claudia Cava, Isabella Castiglioni
The R Journal
High-throughput genomic technologies bring to light a comprehensive hallmark of molecular changes of a disease. It is increasingly evident that genes are not isolated from each other and the identification of a gene signature can only partially elucidate the de-regulated biological functions in a disease. The comprehension of how groups of genes (pathways) are related to each other (pathway-cross talk) could explain biological mechanisms causing diseases. Biological pathways are important tools to identify gene interactions and decrease the large number of genes to be studied by partitioning them into smaller groups. Furthermore, recent scientific studies have demonstrated that an integration …
Whats For Dynr: A Package For Linear And Nonlinear Dynamic Modeling In R,
2019
ACT, Inc.
Whats For Dynr: A Package For Linear And Nonlinear Dynamic Modeling In R, Lu Ou, Michael D. Hunter, Sy-Miin Chow
The R Journal
Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete- and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-to-learn model specification functions in R. We present …
Simcorrmix: Simulation Of Correlated Data With Multiple Variable Types Including Continuous And Count Mixture Distributions,
2019
University of Alabama at Birmingham
Simcorrmix: Simulation Of Correlated Data With Multiple Variable Types Including Continuous And Count Mixture Distributions, Allison Fialkowski, Hemant Tiwari
The R Journal
The SimCorrMix package generates correlated continuous (normal, non-normal, and mixture), binary, ordinal, and count (regular and zero-inflated, Poisson and Negative Binomial) variables that mimic real-world data sets. Continuous variables are simulated using either Fleishman’s third-order or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture distributions, and the target correlation matrix is specified in terms of correlations with components. However, the package contains functions to approximate expected correlations with continuous mixture variables. There are two simulation pathways which calculate intermediate correlations involving count variables differently, increasing accuracy under a wide range of parameters. The package …
