Open Source Compiler for SAS language/ GNU -DAP

A Bold GNU Head
Image via Wikipedia

I am still testing this out.

But if you know bit more about make and .compile in Ubuntu check out

I loved the humorous introduction

Dap is a small statistics and graphics package based on C. Version 3.0 and later of Dap can read SBS programs (based on the utterly famous, industry standard statistics system with similar initials – you know the one I mean)! The user wishing to perform basic statistical analyses is now freed from learning and using C syntax for straightforward tasks, while retaining access to the C-style graphics and statistics features provided by the original implementation. Dap provides core methods of data management, analysis, and graphics that are commonly used in statistical consulting practice (univariate statistics, correlations and regression, ANOVA, categorical data analysis, logistic regression, and nonparametric analyses).

Anyone familiar with the basic syntax of C programs can learn to use the C-style features of Dap quickly and easily from the manual and the examples contained in it; advanced features of C are not necessary, although they are available. (The manual contains a brief introduction to the C syntax needed for Dap.) Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.

I wrote Dap to use in my statistical consulting practice because the aforementioned utterly famous, industry standard statistics system is (or at least was) not available on GNU/Linux and costs a bundle every year under a lease arrangement. And now you can run programs written for that system directly on Dap! I was generally happy with that system, except for the graphics, which are all but impossible to use,  but there were a number of clumsy constructs left over from its ancient origins. output

  • Unbalanced ANOVA
  • Crossed, nested ANOVA
  • Random model, unbalanced
  • Mixed model, balanced
  • Mixed model, unbalanced
  • Split plot
  • Latin square
  • Missing treatment combinations
  • Linear regression
  • Linear regression, model building
  • Ordinal cross-classification
  • Stratified 2×2 tables
  • Loglinear models
  • Logit  model for linear-by-linear association
  • Logistic regression
  • Copyright © 2001, 2002, 2003, 2004 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA

    sounds too good to be true- GNU /DAP joins WPS workbench and Dulles Open’s Carolina as the third SAS language compiler (besides the now defunct BASS software) see


    Also see

    Dap was written to be a free replacement for SAS, but users are assumed to have a basic familiarity with the C programming language in order to permit greater flexibility. Unlike R it has been designed to be used on large data sets.

    It has been designed so as to cope with very large data sets; even when the size of the data exceeds the size of the computer’s memory

    R Commander Plugins-20 and growing!

    First graphical user interface in 1973.
    Image via Wikipedia
    R Commander Extensions: Enhancing a Statistical Graphical User Interface by extending menus to statistical packages

    R Commander ( see paper by Prof J Fox at ) is a well known and established graphical user interface to the R analytical environment.
    While the original GUI was created for a basic statistics course, the enabling of extensions (or plug-ins ) has greatly enhanced the possible use and scope of this software. Here we give a list of all known R Commander Plugins and their uses along with brief comments.

    1. DoE –
    2. doex
    3. EHESampling
    4. epack-
    5. Export-
    6. FactoMineR
    7. HH
    8. IPSUR
    9. MAc-
    10. MAd
    11. orloca
    12. PT
    13. qcc- and
    14. qual
    15. SensoMineR
    16. SLC
    17. sos
    18. survival-
    19. SurvivalT
    20. Teaching Demos

    Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
    Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.

    However the notable E Plugins are
    1) DoE for Design of Experiments-
    Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
    factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
    2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
    3) qcc -GUI for  Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
    4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
    and following Models -Arima, garch, HoltWinters
    5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
    via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
    output to formats other than ASCII text and to provide R novices with an easy-to-use,
    easy-to-access reference on exporting R objects to formats suited for printed output. The
    package documentation contains several pointers on creating reports, either by using
    conventional word processors or LaTeX/LyX.
    6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
    Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
    graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
    R and the MAc package. The MAc package itself contains a variety of useful functions for
    conducting a research synthesis with correlational data. One of the unique features of the MAc
    package is in its integration of user-friendly functions to complete the majority of statistical steps
    involved in a meta-analysis with correlations. It uses recommended procedures as described in
    The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

    A query to help for ??Rcmdrplugins reveals the following information which can be quite overwhelming given that almost 20 plugins are now available-

    Glossary for DoE terminology as used in
    RcmdrPlugin.DoE Linear Model Dialog for
    experimental data
    RcmdrPlugin.DoE response surface model Dialog
    for experimental data
    R-Commander plugin package that implements
    design of experiments facilities from packages
    DoE.base, FrF2 and DoE.wrapper into the
    Functions used in menus
    Internal RcmdrPlugin.doex objects
    Install the DOEX Rcmdr Plug-In
    Internal functions for menu system of
    Help with EHES sampling
    Graphically export objects to LaTeX or HTML
    Internal RcmdrPlugin.FactoMineR objects
    Graphical User Interface for FactoMineR
    An IPSUR Plugin for the R Commander
    Meta-Analysis with Correlations (MAc) Rcmdr
    Meta-Analysis with Mean Differences (MAd) Rcmdr
    RcmdrPlugin.orloca: A GUI for orloca-package
    (internal functions)
    RcmdrPlugin.orloca: A GUI for orloca-package Una interfaz grafica
    para el paquete orloca
    Install the Demos Rcmdr Plug-In
    Internal RcmdrPlugin.qual objects
    Install the quality Rcmdr Plug-In
    Internal RcmdrPlugin.SensoMineR objects
    Graphical User Interface for SensoMineR
    RcmdrPlugin.SLC: A GUI for slc-package
    (internal functions)
    RcmdrPlugin.SLC: A GUI for SLC R package
    Efficiently search R Help pages
    RcmdrPlugin.steepness: A GUI for
    steepness-package (internal functions)
    RcmdrPlugin.steepness: A GUI for steepness R
    Internal RcmdrPlugin.survival Objects
    Rcmdr Plug-In Package for the survival Package
    Install the Demos Rcmdr Plug-In


    Tale of Two Analytical Interfaces

    Occam’s razor (or Ockham’s razor[1]) is often expressed in Latin as the lex parsimoniae(translating to the law of parsimonylaw of economy or law of succinctness). The principle is popularly summarized as “the simplest explanation is more likely the correct one.

    Using a simple screenshot- you can see Facebook Analytics for a Facebook page is simpler at explaining who is coming to visit rather than Google Analytics Dashboard (which has not seen the attention of a Visual UI or Graphic Redesign)

    And if Facebook is going to take over the internet, well it is definitely giving better analytics in the process. What do you think?

    Which Interface is simpler- and gives you better targeting. Ignore the numbers and just see the metrics measured and the way they are presented. Coincidently R is used at Facebook a lot (which has given the jjplot package)- and Google has NOT INVESTED MAJOR MONEY in creating Premium R Packages or Big Data Packages. I am talking investment at the scale Google is known for- not measly meetups.

    (the summer of code dont count- it is for students mostly)

    (but thanks for the Pizza G Men- and maybe revise that GA interface by putting a razor to some metrics)

    GA vs Facebook Analytics