Abstract:

Network analysis is one of the most widely used techniques in many areas of modern science. Most existing tools for that purpose are limited to drawing networks and computing their basic general characteristics. The user is not able to interactively and graphically manipulate the networks, select and explore subgraphs using other statistical and data mining techniques, add and plot various other data within the graph, and so on. In this paper we present a tool that addresses these challenges, an add-on for exploration of networks within the general component-based environment Orange.

Abstract:

We describe an R package cts for fitting a modified form of continuous time autoregressive model, which can be particularly useful with unequally sampled time series. The estimation is based on the application of the Kalman filter. The paper provides the methods and algorithms implemented in the package, including parameter estimation, spectral analysis, forecasting, model checking and Kalman smoothing. The package contains R functions which interface underlying Fortran routines. The package is applied to geophysical and medical data for illustration.

Abstract:

Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions.

This paper describes the R package GA, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods. Several examples are discussed, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions. (This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.)

Abstract:

We present the FRB package for R, which implements the fast and robust bootstrap. This method constitutes an alternative to ordinary bootstrap or asymptotic inference procedures when using robust estimators such as S-, MM- or GS-estimators. The package considers three multivariate settings: principal components analysis, Hotelling tests and multivariate regression. It provides both the robust point estimates and uncertainty measures based on the fast and robust bootstrap. In this paper we give some background on the method, discuss the implementation and provide various examples.

Abstract:

stpp is an R package for analyzing, simulating and displaying space-time point patterns. It covers many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena. The package also includes estimators of the space-time inhomogeneous K-function and pair correlation function. stpp is the first dedicated unified computational environment in the area of spatio-temporal point processes. In this paper we describe space-time point processes and introduce the package stpp to new users.

Abstract:

Animated graphs that demonstrate statistical ideas and methods can both attract interest and assist understanding. In this paper we first discuss how animations can be related to some statistical topics such as iterative algorithms, random simulations, (re)sampling methods and dynamic trends, then we describe the approaches that may be used to create animations, and give an overview to the R package animation, including its design, usage and the statistical topics in the package. With the animation package, we can export the animations produced by R into a variety of formats, such as a web page, a GIF animation, a Flash movie, a PDF document, or an MP4/AVI video, so that users can publish the animations fairly easily. The design of this package is flexible enough to be readily incorporated into web applications, e.g., we can generate animations online with Rweb, which means we do not even need R to be installed locally to create animations. We will show examples of the use of animations in teaching statistics and in the presentation of statistical reports using Sweave or knitr. In fact, this paper itself was written with the knitr and animation package, and the animations are embedded in the PDF document, so that readers can watch the animations in real time when they read the paper (the Adobe Reader is required).

Animations can add insight and interest to traditional static approaches to teaching statistics and reporting, making statistics a more interesting and appealing subject.

Abstract:

MIXREGLS is a program which provides estimates for a mixed-effects location scale model assuming a (conditionally) normally-distributed dependent variable. This model can be used for analysis of data in which subjects may be measured at many observations and interest is in modeling the mean and variance structure. In terms of the variance structure, covariates can by specified to have effects on both the between-subject and within-subject variances. Another use is for clustered data in which subjects are nested within clusters (e.g. clinics, hospitals, schools, etc.) and interest is in modeling the between-cluster and within-cluster variances in terms of covariates. MIXREGLS was written in Fortran and uses maximum likelihood estimation, utilizing both the EM algorithm and a Newton-Raphson solution. Estimation of the random effects is accomplished using empirical Bayes methods. Examples illustrating stand-alone usage and features of MIXREGLS are provided, as well as use via the SAS and R software packages.

Abstract:

We illustrate how to fit multilevel models in the MLwiN package seamlessly from within Stata using the Stata program runmlwin. We argue that using MLwiN and Stata in combination allows researchers to capitalize on the best features of both packages. We provide examples of how to use runmlwin to fit continuous, binary, ordinal, nominal and mixed response multilevel models by both maximum likelihood and Markov chain Monte Carlo estimation.

Abstract:

We propose an algorithm for evaluation of the cumulative bivariate normal distribution, building upon Marsaglia's ideas for evaluation of the cumulative univariate normal distribution. The algorithm delivers competitive performance and can easily be extended to arbitrary precision.

Abstract:

The basic purpose of the economic design of the control charts is to find the optimum control charts parameters to minimize the process cost. In this paper, an R package, edcc (economic design of control charts), which provides a numerical method to find the optimum chart parameters is presented using the unified approach of the economic design. Also, some examples are given to illustrate how to use this package. The types of the control chart available in the edcc package are X̅, CUSUM (cumulative sum), and EWMA (exponentially-weighted moving average) control charts.

Abstract:

Moult is the process by which birds replace their feathers. It is a costly process in terms of energy and reduced flight ability but necessary for the maintenance of the plumage and its functions. Because birds generally avoid to moult while engaged with other energy demanding activities such as breeding and migration, the analysis of moult data gives insight into how birds fit this life stage into the annual cycle, on time constraints in the annual cycle, and on the effects of environmental variables on the timing of moult. The analysis of moult data requires non-standard statistical techniques. More than 20~years ago Underhill and Zucchini developed a likelihood approach for estimating duration, mean start date and variation in start date of a population of moulting birds. However, use of these models has been limited, mainly due to the lack of user-friendly software. The moult package for R implements the Underhill-Zucchini models, allowing the user to specify moult models in a regression type formula. In addition the functions allow the moult parameters (duration, and mean and variation in start date) to depend on explanatory variables. We here describe the package, give a brief summary of the theory and illustrate the models on two datasets included in the package.

Abstract:

Cloud computing is a relatively new technology that facilitates collaborative creation and modification of documents over the internet in real time. Here we provide an introductory assessment of the available statistical functions in three leading cloud spreadsheets namely Google Spreadsheet, Microsoft Excel Web App, and Zoho Sheet. Our results show that the developers of cloud-based spreadsheets are not performing basic quality control, resulting in statistical computations that are misleading and erroneous. Moreover, the developers do not provide sufficient information regarding the software and the hardware, which can change at any time without notice. Indeed, rerunning the tests after several months we obtained different and sometimes worsened results.

Abstract:

Identifying the language used will typically be the first step in most natural language processing tasks. Among the wide variety of language identification methods discussed in the literature, the ones employing the Cavnar and Trenkle (1994) approach to text categorization based on character n-gram frequencies have been particularly successful. This paper presents the R extension package textcat for n-gram based text categorization which implements both the Cavnar and Trenkle approach as well as a reduced n-gram approach designed to remove redundancies of the original approach. A multi-lingual corpus obtained from the Wikipedia pages available on a selection of topics is used to illustrate the functionality of the package and the performance of the provided language identification methods.

Abstract:

The RcppEigen package provides access from R (R Core Team 2012a) to the Eigen (Guennebaud, Jacob, and others 2012) C++ template library for numerical linear algebra. Rcpp (Eddelbuettel and François 2011, 2012) classes and specializations of the C++ templated functions as and wrap from Rcpp provide the "glue" for passing objects from R to C++ and back. Several introductory examples are presented. This is followed by an in-depth discussion of various available approaches for solving least-squares problems, including rank-revealing methods, concluding with an empirical run-time comparison. Last but not least, sparse matrix methods are discussed.