Journal: Journal of Statistical Software

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Publisher

American Statistical Association

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ISSN

1548-7660

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2008 - 200912010 - 20186
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Publications 1 - 7 of 7
  • Nested Archimedean Copulas Meet R: The nacopula Package
    Item type: Journal Article
    Hofert, Marius; Mächler, Martin (2011)
    Journal of Statistical Software
    The package nacopula provides procedures for constructing nested Archimedean copulas in any dimensions and with any kind of nesting structure, generating vectors of random variates from the constructed objects, computing function values and probabilities of falling into hypercubes, as well as evaluation of characteristics such as Kendall's tau and the tail-dependence coefficients. As by-products, algorithms for various distributions, including exponentially tilted stable and Sibuya distributions, are implemented. Detailed examples are given.
  • Fitting Linear Mixed-Effects Models Using lme4
    Item type: Journal Article
    Bates, Douglas; Mächler, Martin; Bolker, Ben; et al. (2015)
    Journal of Statistical Software
    Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.
  • Nonparametric Econometrics: The np Package
    Item type: Journal Article
    Hayfield, Tristen; Racine, Jeffrey S. (2008)
    Journal of Statistical Software
    We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.
  • Rectangular Statistical Cartograms in R: The recmap Package
    Item type: Journal Article
    Panse, Christian (2018)
    Journal of Statistical Software
    Cartogram drawing is a technique for showing geography-related statistical information, such as demographic and epidemiological data. The idea is to distort a map by resizing its regions according to a statistical parameter by keeping the map recognizable. This article describes an R package implementing an algorithm called RecMap which approximates every map region by a rectangle where the area corresponds to the given statistical value (maintain zero cartographic error). The package implements the computationally intensive tasks in C++. This paper's contribution is that it demonstrates on real and synthetic maps how package recmap can be used, how it is implemented and how it is used with other statistical packages.
  • Parallel and Other Simulations in R Made Easy: An End-to-End Study
    Item type: Journal Article
    Hofert, Marius; Mächler, Martin (2016)
    Journal of Statistical Software
    It is shown how to set up, conduct, and analyze large simulation studies with the new R package simsalapar (= simulations simplified and launched parallel). A simulation study typically starts with determining a collection of input variables and their values on which the study depends. Computations are desired for all combinations of these variables. If conducting these computations sequentially is too time-consuming, parallel computing can be applied over all combinations of select variables. The final result object of a simulation study is typically an array. From this array, summary statistics can be derived and presented in terms of flat contingency or LATEX tables or visualized in terms of matrix-like figures. The R package simsalapar provides several tools to achieve the above tasks. Warnings and errors are dealt with correctly, various seeding methods are available, and run time is measured. Furthermore, tools for analyzing the results via tables or graphics are provided. In contrast to rather minimal examples typically found in R packages or vignettes, an end-to-end, not-so-minimal simulation problem from the realm of quantitative risk management is given. The concepts presented and solutions provided by simsalapar may be of interest to students, researchers, and practitioners as a how-to for conducting realistic, large-scale simulation studies in R.
  • spate: An R Package for Spatio-Temporal Modeling with a Stochastic Advection-Diffusion Process
    Item type: Journal Article
    Sigrist, Fabio; Künsch, Hans R.; Stahel, Werner A. (2015)
    Journal of Statistical Software
    The R package spate implements methodology for modeling of large space-time data sets. A spatio-temporal Gaussian process is defined through a stochastic partial differential equation (SPDE) which is solved using spectral methods. In contrast to the traditional geostatistical way of relying on the covariance function, the spectral SPDE approach is computationally tractable and provides a realistic space-time parametrization. This package aims at providing tools for simulating and modeling of spatio-temporal processes using an SPDE based approach. The package contains functions for obtaining parametrizations, such as propagator or innovation covariance matrices, of the spatio-temporal model. This allows for building customized hierarchical Bayesian models using the SPDE based model at the process stage. The functions of the package then provide computationally efficient algorithms needed for doing inference with the hierarchical model. Furthermore, an adaptive Markov chain Monte Carlo (MCMC) algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. This function is flexible and allows for application specific customizing. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Spatio-temporal covariates can be included in the model through a regression term.
  • Causal Inference Using Graphical Models with the R Package pcalg
    Item type: Journal Article
    Kalisch, Markus; Mächler, Martin; Colombo, Diego; et al. (2012)
    Journal of Statistical Software
    The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this document, we give a brief overview of the methodology, and demonstrate the package’s functionality in both toy examples and applications.
Publications 1 - 7 of 7