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.
Develve – Statistical software for fast and easy interpretation of experimental data in science and R&D in a technical environment. This statistical package helps with analysis and prevents making false assumptions. In short it makes statistics faster and easier, suitable for less experience users but advanced enough for more demanding users.
Develve has no deep hidden menus, everything is directly accessible and the results are directly visible, to improve the productivity. For instance the result graphs are easily scrollable and with a click on a graph a bigger version will pop up. When comparing different datasets it is directly indicated if the difference in average and variation is significant and if the sample size is big enough to prevent false assumptions. For a Design of Experiments Develve helps to create a test matrix. When a factor is not in balance develve will detect this.
MacAnova is a free, open source, interactive statistical analysis program for Windows, Macintosh, and Linux written by Gary W. Oehlert and Christopher Bingham, both of the School of Statistics, University of Minnesota. In spite of its name, MacAnova is not just for Macintosh computers and not just for doing Analysis of Variance.
MacAnova has many capabilities but its strengths are analysis of variance and related models, matrix algebra, time series analysis (time and frequency domain), and (to a lesser extent) uni- and multi-variate exploratory statistics. The current version is 5.05 release 1. Core MacAnova has a functional/command oriented interface, but an increasing number of capabilities are available through a menu/dialog/mouse type interface. Although the language and syntax are S-like (for those of you familiar with S, S-Plus or R), MacAnova is not S or R.
R is probably the most sophisticated statistics software available. The learning curve is fairly steep, but the availability of hundreds of add-on packages mean that almost anything can be accomplished. R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc.
Salstat – Years of experience teaching undergraduates and postgraduates struggling to analyse experimental statistics on computer has showed us how programs should work with people not despite them. We want people to analyse data with ease through friendly software that augments the way we need to work.
SOFA is a user-friendly statistics, analysis, & reporting program. It is free, with an emphasis on ease of use, learn as you go, and beautiful output. SOFA lets you display results in an attractive format ready to share. And SOFA will help you learn as you go.