Abstract: Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calculations. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It oers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper oers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

Abstract: The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.

Abstract: We present a new distributed programming extension of the R programming language. By tightly coupling R to the well-known ScaLAPACK and MPI libraries, we are able to achieve highly scalable implementations of common statistical methods, allowing the user to analyze bigger datasets with R than ever before. Early benchmarks show great optimism for the project and its future.

Abstract: In this paper, we describe %PROC_R, a SAS macro that enables native R language to be embedded in and executed along with a SAS program in the base SAS environment under Windows OS. This macro executes a user-defined R code in batch mode by calling the unnamed pipe method within base SAS. The R textual and graphical output can be routed to the SAS output window and result viewer, respectively. Also, this macro automatically converts data between SAS datasets and R data frames such that the data and results from each statistical environment can be utilized by the other environment. The objective of this work is to leverage the strength of the R programming language within the SAS environment in a systematic manner. Moreover, this macro helps statistical programmers to learn a new statistical language while staying in a familiar environment.

Abstract: The present work is the result of the investigation realized by the authoresses on programming languages applied to Non Life Actuarial Mathematics (NLAM). The main objective of this paper is to present and to expose the use of the R programming language in NLAM looking at the experience initiated at the University of Barcelona (UB) during 2009-2010. The contribution of the paper consists of the summary and application of libraries realized by different authors on the topics included in NLAM and its improvement with functions of own production. The article includes, for some of the different blocks that are treated inside NLAM at the UB, a small theoretical summary and numerical examples with R, and with real data of insurance portfolios that can be found in basic papers of actuarial science.