Computer Sciences Commons^™

Smm: An R Package For Estimation And Simulation Of Discrete-Time Semi-Markov Models, Vlad Stefan Barbu, Caroline Bérard, Dominique Cellier, Mathilde Sautreuil, Nicolas Vergne

The R Journal

Semi-Markov models, independently introduced by Lévy (1954), Smith (1955) and Takacs (1954), are a generalization of the well-known Markov models. For semi-Markov models, sojourn times can be arbitrarily distributed, while sojourn times of Markov models are constrained to be exponentially distributed (in continuous time) or geometrically distributed (in discrete time). The aim of this paper is to present the R package SMM, devoted to the simulation and estimation of discrete-time multi-state semi-Markov and Markov models. For the semi-Markov case we have considered: parametric and non-parametric estimation; with and without censoring at the beginning and/or at the end of sample …

Fica: Fastica Algorithms And Their Improved Variants, Jari Miettinen, Klaus Nordhausen, Sara Taskinen

The R Journal

In independent component analysis (ICA) one searches for mutually independent nongaussian latent variables when the components of the multivariate data are assumed to be linear combinations of them. Arguably, the most popular method to perform ICA is FastICA. There are two classical versions, the deflation-based FastICA where the components are found one by one, and the symmetric FastICA where the components are found simultaneously. These methods have been implemented previously in two R packages, fastICA and ica. We present the R package fICA and compare it to the other packages. Additional features in fICA include optimization of the extraction order …

Snowboot: Bootstrap Methods For Network Inference, Yuzhou Chen, Yulia R. Gel, Vyacheslav Lyubchich, Kusha Nezafati

The R Journal

Complex networks are used to describe a broad range of disparate social systems and natural phenomena, from power grids to customer segmentation to human brain connectome. Challenges of parametric model specification and validation inspire a search for more data-driven and flexible nonparametric approaches for inference of complex networks. In this paper we discuss methodology and R implementation of two bootstrap procedures on random networks, that is, patchwork bootstrap of Thompson et al. (2016) and Gel et al. (2017) and vertex bootstrap of Snijders and Borgatti (1999). To our knowledge, the new R package snowboot is the first implementation of the …

Addhaz: Contribution Of Chronic Diseases To The Disability Burden Using R, Renata Tiene De Carvalho Yokota, Caspar Wn Looman, Wilma Johanna Nusselder, Herman Van Oyen, Geert Molenberghs

The R Journal

The increase in life expectancy followed by the burden of chronic diseases contributes to disability at older ages. The estimation of how much chronic conditions contribute to disability can be useful to develop public health strategies to reduce the burden. This paper introduces the R package addhaz, which is based on the attribution method (Nusselder and Looman, 2004) to partition disability into the additive contributions of diseases using cross-sectional data. The R package includes tools to fit the additive hazard model, the core of the attribution method, to binary and multinomial outcomes. The models are fitted by maximizing the …

Stplanr: A Package For Transport Planning, Robin Lovelace, Richard Ellison

The R Journal

Tools for transport planning should be flexible, scalable, and transparent. The stplanr package demonstrates and provides a home for such tools, with an emphasis on spatial transport data and non-motorized modes. The stplanr package facilitates common transport planning tasks including: downloading and cleaning transport datasets; creating geographic “desire lines” from origin-destination (OD) data; route assignment, locally and interfaces to routing services such as CycleStreets.net; calculation of route segment attributes such as bearing and aggregate flow; and ‘travel watershed’ analysis. This paper demonstrates this functionality using reproducible examples on real transport datasets. More broadly, the experience of developing and using R …

Changes In R, R Core Team

The R Journal

CHANGES IN R 3.5.2

CHANGES IN R 3.5.1

Conference Report: Why R? 2018, Michał Burdukiewicz, Marta Karas, Leon Eyrich Jessen, Marcin KosińSki, Bernd Bischl, Stefan Rödiger

The R Journal

The primary purpose of the Why R? 2018 conference was to provide R programming language enthusiasts with an opportunity to meet and discuss experiences in R software development and analysis applications, for both academia and industry professionals. The event was held 2-5 August, 2018 in a city of Wroclaw, a strong academic and business center of Poland. The total of approximately 250 people from 6 countries attended the main conference event. Additionally, approximately 540 R users attended the pre-meetings in eleven cities across Europe (Figure 2).

Conference Report: Latinr 2018, Laura Acion, Natalia Da Silva, Riva Quiroga

The R Journal

LatinR <- Latin American Conference about the Use of R in Research + Development (LatinR) was an international conference whose goal was bringing together the Latin Ameri can R community. The inagural LatinR took place at the Universidad de Palermo in Buenos Aires, Argentina, on September 3 to 5, 2018. About 100 participants from more than 10 differ ent countries (e.g., Argentina, Uruguay, Chile, Peru, Ecuador, Brazil, Costa Rica, Venezuela, Spain, United States, Canada) attended LatinR.

LatinR will be an annual meeting that will rotate among different countries in Latin America. LatinR 2019 will be hosted by the Universidad Católica de Chile in Santiago de Chile on September 25 to 27

Shinyitemanalysis For Teaching Psychometrics And To Enforce Routine Analysis Of Educational Tests, Patrícia Martinková, Adéla Drabinová

The R Journal

This work introduces ShinyItemAnalysis, an R package and an online shiny application for psychometric analysis of educational tests and items. ShinyItemAnalysis covers a broad range of psychometric methods and offers data examples, model equations, parameter estimates, interpretation of results, together with a selected R code, and is therefore suitable for teaching psychometric concepts with R. Furthermore, the application aspires to be an easy-to-use tool for analysis of educational tests by allowing the users to upload and analyze their own data and to automatically generate analysis reports in PDF or HTML. We argue that psychometric analysis should be a routine …

Bnclassify: Learning Bayesian Network Classifiers, Bojan Mihaljević, Concha Bielza, Pedro Larrañaga

The R Journal

The bnclassify package provides state-of-the art algorithms for learning Bayesian network classifiers from data. For structure learning it provides variants of the greedy hill-climbing search, a well-known adaptation of the Chow-Liu algorithm and averaged one-dependence estimators. It provides Bayesian and maximum likelihood parameter estimation, as well as three naive-Bayes specific methods based on discriminative score optimization and Bayesian model averaging. The implementation is efficient enough to allow for time-consuming discriminative scores on medium sized data sets. The bnclassify package provides utilities for model evaluation, such as cross-validated accuracy and penalized log-likelihood scores, and analysis of the underlying networks, including network …

Networktoolbox: Methods And Measures For Brain, Cognitive, And Psychometric Network Analysis In R, Alexander P. Christensen

The R Journal

This article introduces the NetworkToolbox package for R. Network analysis offers an intuitive perspective on complex phenomena via models depicted by nodes (variables) and edges (correlations). The ability of networks to model complexity has made them the standard approach for modeling the intricate interactions in the brain. Similarly, networks have become an increasingly attractive model for studying the complexity of psychological and psychopathological phenomena. NetworkToolbox aims to provide researchers with state-of-the-art methods and measures for estimating and analyzing brain, cognitive, and psychometric networks. In this article, I introduce NetworkToolbox and provide a tutorial for applying some the package’s functions to …

Dynamic Simulation And Testing For Single-Equation Cointegrating And Stationary Autoregressive Distributed Lag Models, Soren Jordan, Andrew Q. Philips

The R Journal

While autoregressive distributed lag models allow for extremely flexible dynamics, interpreting the substantive significance of complex lag structures remains difficult. In this paper we discuss dynamac (dynamic autoregressive and cointegrating models), an R package designed to assist users in estimating, dynamically simulating, and plotting the results of a variety of autoregressive distributed lag models. It also contains a number of post-estimation diagnostics, including a test for cointegration for when researchers are estimating the error-correction variant of the autoregressive distributed lag model.

Bnsp: An R Package For Fitting Bayesian Semiparametric Regression Models And Variable Selection, Georgios Papageorgiou

The R Journal

The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to represent semiparametric covariate effects in the mean and variance functions, and spike-slab priors to perform selection and regularization of the estimated effects. In addition to the main function that performs posterior sampling, the package includes functions for assessing convergence of the sampler, summarizing model fits, visualizing covariate effects and obtaining predictions for new responses or their means given feature/covariate vectors.

Basis-Adaptive Selection Algorithm In Dr-Package, Jae Keun Yoo

The R Journal

Sufficient dimension reduction (SDR) turns out to be a useful dimension reduction tool in high-dimensional regression analysis. Weisberg (2002) developed the dr-package to implement the four most popular SDR methods. However, the package does not provide any clear guidelines as to which method should be used given a data. Since the four methods may provide dramatically different dimension reduction results, the selection in the dr-package is problematic for statistical practitioners. In this paper, a basis-adaptive selection algorithm is developed in order to relieve this issue. The basic idea is to select an SDR method that provides the highest …

Rcss: R Package For Optimal Convex Stochastic Switching, Juri Hinz, Jeremy Yee

The R Journal

The R package rcss provides users with a tool to approximate the value functions in the Bellman recursion under certain assumptions that guarantee desirable convergence properties. This R package represents the first software implementation of these methods using matrices and nearest neighbors. This package also employs a pathwise dynamic method to gauge the quality of these value function approximations. Statistical analysis can be performed on the results to obtain other useful practical insights. This paper describes rcss version 1.6.

Idmtpreg: Regression Model For Progressive Illness Death Data, Leyla Azarang, Manuel Oviedo De La Fuente

The R Journal

The progressive illness-death model is frequently used in medical applications. For example, the model may be used to describe the disease process in cancer studies. We have developed a new R package called idmTPreg to estimate regression coefficients in datasets that can be described by the progressive illness-death model. The motivation for the development of the package is a recent contribution that enables the estimation of possibly time-varying covariate effects on the transition probabilities for a progressive illness-death data. The main feature of the package is that it befits both non-Markov and Markov progressive illness-death data. The package implements the …

Conference Report: R / Medicine Report, Joseph Rickert, Naras Balasubramanian, Michael Kane

The R Journal

R has found widespread use and is flourishing in bioinformatics, the pharmaceutical industry, clinical trials, and basic science labs. While R is being adopted in clinical informatics, its potential has not yet been realized. We believe R will fundamentally transform the space because of its strengths in making new methods available, the availability of tools for reproducible research, its interfaces to other languages, and it’s ability to disseminate new approaches through web interfaces and packaging for rapid prototyping of ideas and implementations.

Moreover, while large number of clinicians, scientists, and statisticians contribute to data driven medical science, communication between individuals …

Conference Report: Ser Iii, Ariel Levy, Luciane F. Alcoforado, Orlando Celso Longo

The R Journal

SER is a multidisciplinary event, which integrates professionals, students, and practioners from most diversified knowledge areas who make use of data analysis. The first edition took place on May 2016 as the initiative of a group of professors from the Fluminense Federal University, partners of other Institutions, and was supported by CAPES (Coordination for higher Education Staff Development). SER event was recognized by the R foundation (2018)1 for its pioneering in Latin America in bringing together an expressive number of R users.

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

The Bioconductor project provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.8 was released on 31 October, 2018. It is compatible with R 3.5.2 and consists of 1649 software packages, 360 experiment data packages, and 941 up-to-date annotation packages. The release announcement includes descriptions of 95 new software packages and updated NEWS files for many additional packages.

Navigating The R Package Universe, Julia Silge, John C. Nash, Spencer Graves

The R Journal

Today, the enormous number of contributed packages available to R users outstrips any given user’s ability to understand how these packages work, their relative merits, or how they are related to each other. We organized a plenary session at useR!2017 in Brussels for the R community to think through these issues and ways forward. This session considered three key points of discussion. Users can navigate the universe of R packages with (1) capabilities for directly searching for R packages, (2) guidance for which packages to use, e.g., from CRAN Task Views and other sources, and (3) access to commoninterfaces for …

Consistency Cubes: A Fast, Efficient Method For Exact Boolean Minimization, Adrian Dusa

The R Journal

A lot of effort has been spent over the past few decades in the QCA methodology field, to develop efficient Boolean minimization algorithms to derive an exact,and more importantly complete list of minimal prime implicants that explain the initial, observed positive configurations.

As the complexity grows exponentially with every new condition, the required computer memory goes past the current computer resources and the polynomial time required to solve this problem quickly grows towards infinity.

This paper introduces a new alternative to the existing non-polynomial attempts. It completely solves the memory problem, and preliminary tests show it is exponentially hundreds of …

Conference Report: R / Pharma 2018, Joseph Rickert

The R Journal

The R / Pharma conference began as grass-roots initiative led by data scientists working in the pharmaceutical industry to promote the use of R in Pharma, and to establish and share best practices. The founding members organized the project as an R Consortium working group, and undertook the ambitious task of launching an annual conference envisioned as a relatively small, collegial, industry-oriented event with a strong scientific program.

Forecast Combinations In R Using The Forecastcomb Package, Christoph E. Weiss, Eran Raviv, Gernot Roetzer

The R Journal

This paper introduces the R package ForecastComb. The aim is to provide researchers and practitioners with a comprehensive implementation of the most common ways in which forecasts can be combined. The package in its current version covers 15 popular estimation methods for creating a combined forecasts – including simple methods, regression-based methods, and eigenvector-based methods. It also includes useful tools to deal with common challenges of forecast combination (e.g., missing values in component forecasts, or multicollinearity), and to rationalize and visualize the combination results.

The Politeness Package: Detecting Politeness In Natural Language, Michael Yeomans, Alejandro Kantor, Dustin Tingley

The R Journal

This package provides tools to extract politeness markers in English natural language. It also allows researchers to easily visualize and quantify politeness between groups of documents. This package combines and extends prior research on the linguistic markers of politeness (Brown and Levinson, 1987; Danescu-Niculescu-Mizil et al., 2013; Voigt et al., 2017). We demonstrate two applications for detecting politeness in natural language during consequential social interactions— distributive negotiations, and speed dating.

Jsr223: A Java Platform Integration For R With Programming Languages Groovy, Javascript, Jruby, Jython, And Kotlin, Floid R. Gilbert, David B. Dahl

The R Journal

The R package jsr223 is a high-level integration for five programming languages in the Java platform: Groovy, JavaScript, JRuby, Jython, and Kotlin. Each of these languages can use Java objects in their own syntax. Hence, jsr223 is also an integration for R and the Java platform. It enables developers to leverage Java solutions from within R by embedding code snippets or evaluating script f iles. This approach is generally easier than rJava’s low-level approach that employs the Java Native Interface. jsr223’s multi-language support is dependent on the Java Scripting API: an implementation of “JSR-223: Scripting for the Java …

Rcppmsgpack: Messagepack Headers And Interface Functions For R, Travers Ching, Dirk Eddelbuettel

The R Journal

MessagePack, or MsgPack for short, or when referring to the implementation, is an efficient binary serialization format for exchanging data between different programming languages. The RcppMsgPack package provides R with both the MessagePack C++ header files, and the ability to access, create and alter MessagePack objects directly from R. The main driver functions of the R interface are two functions msgpack_pack and msgpack_unpack. The function msgpack_pack serializes Robjects to a raw MessagePack message. The function msgpack_unpack de-serializes MessagePack messages back into R objects. Several helper functions are available to aid in processing and formatting data including msgpack_simplify, msgpack_format and msgpack_map

Changes On Cran, Kurt Hornik, Uwe Ligges, Achim Zeileis

The R Journal

In the past 6 months, 1029 new packages were added to the CRAN package repository. 68 packages were unarchived, 122 archived, and one removed. The following shows the growth of the number of active packages in the CRAN package repository

Rfsa: An R Package For Finding Best Subsets And Interactions, Joshua Lambert, Liyu Gong, Corrine F. Elliott, Katherine Thompson, Arnold Stromberg

The R Journal

Herein we present the R package rFSA, which implements an algorithm for improved variable selection. The algorithm searches a data space for models of a user-specified form that are statistically optimal under a measure of model quality. Many iterations afford a set of feasible solutions (or candidate models) that the researcher can evaluate for relevance to his or her questions of interest. The algorithm can be used to formulate new or to improve upon existing models in bioinformatics, health care, and myriad other fields in which the volume of available data has outstripped researchers’ practical and computational ability to explore …

Revengc: An R Package To Reverse Engineer Summarized Data, Samantha Duchscherer, Robert Stewart, Marie Urban

The R Journal

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations, such as the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and World Bank. The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers censored and/or decoupled data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Ggplot2 Compatible Quantile-Quantile Plots In, Alexandre Almeida, Adam Loy, Heike Hofmann

The R Journal

Q-Q plots allow us to assess univariate distributional assumptions by comparing a set of quantiles from the empirical and the theoretical distributions in the form of a scatterplot. To aid in the interpretation of Q-Q plots, reference lines and confidence bands are often added. We can also detrend the Q-Q plot so the vertical comparisons of interest come into focus. Various implementations of Q-Q plots exist in R, but none implements all of these features. qqplotr extends ggplot2 to provide a complete implementation of Q-Q plots. This paper introduces the plotting framework provided by qqplotr and provides multiple examples of …