https://www.jstatsoft.org/index.php/jss/issue/feedJournal of Statistical Software2020-02-18T11:32:27+00:00Editorial Officeeditor@jstatsoft.orgOpen Journal Systemshttps://www.jstatsoft.org/index.php/jss/article/view/v092i01Most Likely Transformations: The mlt Package2020-02-18T10:43:29+00:00Torsten HothornTorsten.Hothorn@uzh.chThe mlt package implements maximum likelihood estimation in the class of conditional transformation models. Based on a suitable explicit parameterization of the unconditional or conditional transformation function using infrastructure from package basefun, we show how one can define, estimate, and compare a cascade of increasingly complex transformation models in the maximum likelihood framework. Models for the unconditional or conditional distribution function of any univariate response variable are set-up and estimated in the same computational framework simply by choosing an appropriate transformation function and parameterization thereof. As it is computationally cheap to evaluate the distribution function, models can be estimated by maximization of the exact likelihood, especially in the presence of random censoring or truncation. The relatively dense high-level implementation in the R system for statistical computing allows generalization of many established implementations of linear transformation models, such as the Cox model or other parametric models for the analysis of survival or ordered categorical data, to the more complex situations illustrated in this paper.2020-02-18T11:32:27+00:00Copyright (c) 2020 Torsten Hothornhttps://www.jstatsoft.org/index.php/jss/article/view/v092i02The Calculus of M-Estimation in R with geex2020-02-18T10:44:37+00:00Bradley C. Saulbradleysaul@gmail.comMichael G. Hudgensmhudgens@email.unc.eduM-estimation, or estimating equation, methods are widely applicable for point estimation and asymptotic inference. In this paper, we present an R package that can find roots and compute the empirical sandwich variance estimator for any set of user-specified, unbiased estimating equations. Examples from the M-estimation primer by Stefanski and Boos (2002) demonstrate use of the software. The package also includes a framework for finite sample, heteroscedastic, and autocorrelation variance corrections, and a website with an extensive collection of tutorials.2020-02-18T11:32:27+00:00Copyright (c) 2020 Bradley C. Saul, Michael G. Hudgenshttps://www.jstatsoft.org/index.php/jss/article/view/v092i03PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks2020-02-18T10:47:55+00:00Thong Phamthong.pham@riken.jpPaul Sheridanno@e-mail.providedHidetoshi Shimodairano@e-mail.providedMany real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential attachment function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential attachment function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential attachment function and node fitnesses in a growing network, as well as a number of functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated attachment function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.2020-02-18T11:32:27+00:00Copyright (c) 2020 Thong Pham, Paul Sheridan, Hidetoshi Shimodairahttps://www.jstatsoft.org/index.php/jss/article/view/v092i04Integration of R and Scala Using rscala2020-02-18T10:52:41+00:00David B. Dahldahl@stat.byu.eduThe rscala software is a simple, two-way bridge between R and Scala that allows users to leverage the unique strengths of both languages in a single project. Scala classes can be instantiated from R and Scala methods can be called. Arbitrary Scala code can be executed on-the-fly from within R and callbacks to R are supported. R packages can be developed based on Scala. Conversely, rscala also enables R code to be embedded within a Scala application. The rscala package is available from the Comprehensive R Archive Network (CRAN) and has no dependencies beyond base R and the Scala standard library.2020-02-18T11:32:27+00:00Copyright (c) 2020 David B. Dahlhttps://www.jstatsoft.org/index.php/jss/article/view/v092c01Working with User Agent Strings in Stata: The parseuas Command2020-02-18T10:56:41+00:00Joss Roßmannjoss.rossmann@gesis.orgTobias Gummertobias.gummer@gesis.orgLars Kaczmireklars.kaczmirek@univie.ac.atWith the rising popularity of web surveys and the increasing use of paradata by survey methodologists, assessing information stored in user agent strings becomes inevitable. These data contain meaningful information about the browser, operating system, and device that a survey respondent uses. This article provides an overview of user agent strings, their specific structure and history, how they can be obtained when conducting a web survey, as well as what kind of information can be extracted from the strings. Further, the user written command parseuas is introduced as an efficient means to gather detailed information from user agent strings. The application of parseuas is illustrated by an example that draws on a pooled data set consisting of 29 web surveys.2020-02-18T11:32:27+00:00Copyright (c) 2020 Joss Roßmann, Tobias Gummer, Lars Kaczmirekhttps://www.jstatsoft.org/index.php/jss/article/view/v092b01R Graphics (3rd Edition)2020-02-18T11:31:31+00:00Jose M. Pavíapavia@uv.es2020-02-18T11:32:27+00:00Copyright (c) 2020 Jose M. Pavía