Methods for data analysis in various fields of statistics are becoming increasingly complex and computational. Examples include advanced parametric and semi-parametric models, bootstrap and Monte Carlo techniques, statistical learning algorithms, methods for large data sets (data mining and knowledge discovery), amongst many others. In order to make such methodology available to researchers and practitioners, a clear description of the conceptual tools is required as well as an implementation in statistical software which makes the underlying algorithms applicable to real-world data.
This specialized group is concerned with research related to the design, analysis and implementation of algorithms and software to support statistical methods and applications.
Full papers containing a strong computational or data analytic component will be considered for publication in a Special Issue, or regular issue of Statistical Algorithms and Software of the journal Computational Statistics and Data Analysis. All submissions must contain original unpublished work not being considered for publication elsewhere. Submissions will be refereed according to standard procedures for CSDA.