Recent Publications
Articles
| libstable: Fast, Parallel, and High-Precision Computation of α-Stable Distributions in R, C/C++, and MATLAB | |
| Javier Royuela-del-Val, Federico Simmross-Wattenberg, Carlos Alberola-López |
| Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm | |
| Cécile Proust-Lima, Viviane Philipps, Benoit Liquet |
| pyParticleEst: A Python Framework for Particle-Based Estimation Methods | |
| Jerker Nordh |
| pvclass: An R Package for p Values for Classification | |
| Niki Zumbrunnen, Lutz Dümbgen |
| Rgbp: An R Package for Gaussian, Poisson, and Binomial Random Effects Models with Frequency Coverage Evaluations | |
| Hyungsuk Tak, Joseph Kelly, Carl Morris |
| msBP: An R Package to Perform Bayesian Nonparametric Inference Using Multiscale Bernstein Polynomials Mixtures | |
| Antonio Canale |
| cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models | |
| Francesco Bartolucci, Claudia Pigini |
| PrevMap: An R Package for Prevalence Mapping | |
| Emanuele Giorgi, Peter J. Diggle |
| somoclu: An Efficient Parallel Library for Self-Organizing Maps | |
| Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao |
| KFAS: Exponential Family State Space Models in R | |
| Jouni Helske |
Code Snippets
| Multivariate-From-Univariate MCMC Sampler: The R Package MfUSampler | |
| Alireza S. Mahani, Mansour T. A. Sharabiani |
Book Reviews
| Multivariate Analysis with LISREL | |
| Abdolvahab Khademi |
| Big Data and Social Science - A Practical Guide to Methods and Tools | |
| Stefano M. Iacus |
| Multivariate Statistical Methods: A Primer (4th Edition) | |
| Andreas Rosenblad |
| Introduction to Scientific Programming and Simulation Using R (2nd Edition) | |
| Hakan Demirtas, Rachel Nordgren |
Established in 1996, the Journal of Statistical Software publishes articles, book reviews, code snippets, and software reviews on the subject of statistical software and algorithms. The contents are freely available on-line. For both articles and code snippets the source code is published along with the paper. Statistical software is the key link between statistical methods and their application in practice. Software that makes this link is the province of the journal, and may be realized as, for instance, tools for large scale computing, database technology, desktop computing, distributed systems, the World Wide Web, reproducible research, archiving and documentation, and embedded systems. We attempt to present research that demonstrates the joint evolution of computational and statistical methods and techniques. Implementations can use languages such as C, C++, S, Fortran, Java, PHP, Python and Ruby or environments such as Mathematica, MATLAB, R, S-PLUS, SAS, Stata, and XLISP-STAT.
Announcements
| Changes in editorial team | |
| In response to the continuing success of JSS we have expanded our editorial team aiming to further enhance the quality of publications and lower reviewing times and publication delays. First, Torsten Hothorn has joined as the fourth editor-in-chief. Second, we now have four editorial assistants who help with managing submissions, work flows, editing, technical checks, web services, etc.: Aaron Danielson, Gregor Kastner, Heidi Seibold, Reto Stauffer. Welcome to all the new members of the editorial team! |
|
| JSS Celebrates 20 Years: Special Volume 73 | |
| 2016 marks the 20th anniversary of the Journal of Statistical Software. We celebrate the anniversary with a Festschrift for Jan de Leeuw, the journal's founding editor, which is now online as Special Volume 73 (Editors: Patrick Mair, Katharine Mullen). |
|
| New JSS server | |
| The Journal of Statistical Software moved to a new server based on Open Journal Systems (OJS, http://pkp.sfu.ca/ojs/) which not only hosts published software/manuscripts but also provides a full editorial system. Thus, all submissions, reviews, etc. can now be made through the new OJS system. |
|
| More Announcements... |
Support
|
As a matter of principle, JSS charges no author fees or subscription fees. Our editors, reviewers, and programmers are volunteers. UCLA Statistics and Universität Innsbruck contribute support staff, website maintenance, website hosting, and some graduate student support. Because of our success and growth we do need more resources in the future. You can support us by becoming a member of the Foundation for Open Access Statistics at www.foastat.org, and by contributing on their donation page. |