Current Version: 0.8x (requires OS 10.9+, Windows Vista+, or Linux)
Listing created: 10/2017; program updated 9/2017
Jamovi: A free, open source package, built on top of an R foundation (Thanks, Dr. Kim-Oliver Tietze). Don’t let that put you off: unlike most interfaces for R, Jamovi uses a simple spreadsheet interface with full graphics. You can edit via spreadsheet; and your data, analyses, and options are saved in a single file, so others can reproduce your work. A large number of analyses are easy to find, or you can use R syntax.
We played with the software briefly, and found that the results were attractive (see above), with menus that will be familiar to any SPSS users — and with many options. Unlike SPSS, when you add an option, it doesn’t rerun everything and create a huge amount of new output; for example, in the illustration above, we clicked on the “Adjusted R2” option and it was immediately added to the existing screen. That’s a major improvement.
Speed was quite satisfactory using a short file, but it choked on our four-variable 30,000-case test file.
Data can be imported from CSV; in the future, Excel, SPSS, and LibreOffice are to be supported directly. Installation of the software is by “drag-copy” — drag it out of the download image and into the desired folder. The interface is exactly the same, regardless of platform — Windows, Mac, Linux.
A “syntax mode” (enable it by clicking on the three dots at the right-side of the blue stripe) shows the generated R syntax for each menu command, helping you to learn R syntax or make scripts to reproduce the same actions over and over (a feature introduced long ago, on SPSS 4 — the first SPSS that ran on the Mac.) At the moment, syntax does not allow data importing.
Jamovi is fast, but doesn’t fully use the Mac interface (for example, its menus are kept within its own window instead of at the top of the screen; more annoyingly, the open/save dialogue box is very different, though it does show shortcuts for the documents, downloads, desktop, and home folders.) You can, however, drag and drop data files onto it — saving time.
The program is almost 600 megabytes in size, due largely to the integrated software — R, Electron, Mantle, Python, and ReactiveCoca. The Exploration menu wasn’t functional when we tried it out; and we haven’t yet run it with our large sample dataset to compare results with other software.
Current Version: 0.8x (requires OS 10.11+)
Listing updated: 5/2017; program updated 4/2017
JASP was created as “a low fat alternative to SPSS, a delicious alternative to R.” It is not yet at version 1 and may be used with caution; it also requires the free XQuartz window environment. Though it’s not a native Mac program, it does use the native open/save dialogue box, surprisingly.
The software is designed to look and feel like SPSS, and it does so better than SPSS itself. Though it isn’t native Mac software, it feels more native than SPSS’ officially, and rather poorly ported, versions of the past. Calculations and screen drawing are far, far, far faster than in “real SPSS” — as in, when you select the tests, they might actually be pumped out before your finger is fully off the mouse. This is an incredible effort. Thanks to Prof. Kim-Oliver Tietze for pointing this out to us.
What’s missing in JASP? There are still many procedures to be written, but the biggest user-interface gaps from “actual SPSS” are in the lack of syntax or macros, and the lack of customizability for charts. There do not seem to be plans for syntax.
Current Version: 3.15 / requires OS 10.8-10.11
Listing updated: 5/2017 (program updated March 2017)
“Past is free software for scientific data analysis, with functions for data manipulation, plotting, univariate and multivariate statistics, ecological analysis, time series and spatial analysis, morphometrics and stratigraphy.” That said, Dennis Helsel wrote, “While its name shows its origin (Paleontology), it is a full-fledged stat package which includes multivariate and permutation tests, with a nice interface.”
There was a complete rewrite back in 2013. New features continue to be added with “hundredth-point” releases. Graphics can be sent to PDF. The program allows scripting, to save time. There is also a Windows version.
Current Version: 0.78 (Mac); 0.10.2, source code
Listing updated 5/2/2017
PSPP is a free SPSS clone. Similar to SPSS in most ways, PSPP is particularly aimed at social scientists, business people, and students, with a convenient, easy to learn interface. The project advanced rapidly from 2009 to 2010, but seems to have largely stalled since then; it is still being updated now and then, but progress is slow, and the Mac version is behind the rest.
After running the Mac installer, PSPP will be in the Applications/MacPorts directory, double click, and wait patiently for it to load; this takes a while and may not give any indication of movement. The interface is nearly identical to an old version of SPSS for Windows. The open/save dialogue boxes are painful, but it has SPSS-style data views and imports SPSS data files, long variable names, and variable and value labels.
The speed is much faster than SPSS, with no delays for writing to the output window. Key commands bring up various windows. Common options are included in some dialogue boxes without the need to dig deeper, another improvement over SPSS. On the down side, there are fewer tests and functions, you can copy from the output window but it doesn't show that you're selecting anything, and the output window copies plain-text (space-and-pipe delimited), just as SPSS 4 did. The control key is required instead of the command key, which is awkward on the Mac.
PSPP can perform several data transformation (including recoding, weighting and handling of missing values), compute descriptive statistics (frequencies, descriptive statistics), compute crosstabs and explore tables, T-tests (one sample T-test, independent samples T-test, paired samples T-test) and one-way ANOVA, bivariate correlationlinear regression, factor analysis (Principal Component Analysis and Principal Axis Factoring), Chronbach Alpha (reliability measure), ROC curve and some non-parametric tests (Chi-square and Binominal).
Configurations Available: Mac, Linux, Windows
Current Mac Version: 1.4.6
Listing updated: 4/22/2017
SOFA Statistics (Statistics Open For All) is a graphical-interface statistical package with an emphasis on ease of use, discoverability, and clean reporting. It can connect directly to several database sources, or can use data brought in from spreadsheets. The usual statistical suspects are available, including one-way ANOVA, t-tests, signed ranks, chi-square, and R; nested tables can be produced with row and column percentages, totals, sd, mean, median, and sum.
SOFA Statistics is written in Python, using a wxPython widget toolkit. Statistics come via the Scipy stats module. Analysis and reporting can be automated using Python scripts, either exported from SOFA or written by hand.
Configurations: Mac and Windows
Listing updated: 1/2017
Smith’s Statistical Package is free and user-friendly.
Configurations Available: Intel processors (10.6+)
Current Version: 5.06
Listing updated: 1-2-17
MacAnova is a free, noncommercial, interactive statistical analysis program developed by Gary Oehlert and Christopher Bingham of the University of Minnesota School of Statistics. Their web site notes:
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. MacAnova has a functional/command oriented interface. The Macintosh and Windows versions also have several window/menu/mouse type features. Although the language and syntax are S-like, MacAnova is not S or R.
MacAnova is now Intel native, and there is source code available. We found the program started up very quickly on a Intel Mini and had a fairly good menu system, which output visible code that we could copy and manipulate, or save and run later. It is almost similar to SPSS 4 in that regard, though better integrated into the system and lacking a separate output window. This is certainly worth a download. MacANOVA includes linear model and GLM routines.
Requires: Lion 10.7 or later (older versions available to support just about any system)
Current Version: 2.7.3
Listing updated: 1/2017
Michael McLaughlin’s Regress+ is a free package that includes regression, stochastic modeling, bootstrapping and robust goodness of fit measures. The software and a tutorial are available at the Regress+ web site. Older versions are still available for older operation systems, while version 2.5 is available for OS X and 9.2.
Regress+ 2.7, née Regress+ 3.0, is a complete rewrite; it adds data modeling (equations and distributions), extensive documentation, and publication quality graphics.
This program appears to cover every aspect of regression you can think of. It's graphically oriented but has strong statistics. The code is “more than 100 times faster than before.”
Configurations: Java; should work on Intel and PPC Macs
Current Version: 4.3
Listing updated: 1/2017
Statistics101 is giftware to help teach probability and statistics the easy way—by simulation. “Gain deeper understanding of traditional statistics concepts and methods. Increase your awareness of the role of variability in probability and statistics. Learn and apply simple to very sophisticated statistical techniques without tables or complicated formulas.” Interprets and executes the simple “Resampling Stats” programming language. The original Resampling Stats language and computer program were developed by Dr. Julian Simon and Peter Bruce to teach statistics.
Configurations Available: Mac, Linux, Windows, UNIX (runs on Python)
Current Mac Version:
Listing updated: 1/2/2017
Salstat dates back to the early 2000s and runs on Python; installing the free version on the Mac may require quite a bit of library-and-Python downloading, but a paid version makes everything easy. There is a reward to the work of installation, though, in a free program which makes highly presentable graphics, is relatively easy to use, provides a great deal of descriptive statistics with parametric and nonparametric tests, shows its own source code, does crosstabs, and “charts, imports CSV, HTML, XML, Excel, LibreOffice and SAS file formats, and can even scrape tables of data from web pages.”
Configurations Available: PowerPC (thru 1.7); Universal Binary (2.3+); Linux
Current Version: 3; under active development
Listing updated 5/2013
This is an exceedingly flexible program, with a large number of libraries and built in routines, and the ability to run many S or S-Plus programs. R loads and runs quickly but has a steep learning curve.
R programs and algorithms are distributed by the Comprehensive R Archive Network (CRAN). A simple and somewhat frustrating graphic user interface is included for Mac users; R Commander can be installed using the built-in package installer, which can also install file import features (which aren't installed by default). R Commander is an X11 program, which means it uses an alien interface and has odd open/save dialogues, but if you get past that it offers menu driven commands not dissimilar from, say, SPSS, just a lot more awkward to use, and without an output or data window.
There is also a Java interface, JGR, designed for the Mac. In our experience, it has limited utility.
R has a massive range of tests, PDF and PostScript output, a function to expand zip archives, and numerous other unexpected features. For much more information about R, including advantages, drawbacks, resources, and tips, see our R statistics software for the Mac page.
A person with plans to stay in their career for many years and more time than money may find R to be a fine choice, but it is not for the casual or infrequent researcher.
Note: ADE-4 is a free, noncommercial, interactive statistical analysis program developed by Laboratoire de Biométrie, Génétique et Biologie des Populations in France. While the ADE-4 standalone program has been abandoned, a plug-in for R is available, free, and actively updated (the last update we noted was in April 2009).
Configurations Available: 680x0 (version 3); PowerPC/OS X (version 4); could probably be compiled for Intel
Current Version: 3 / Developer Release 4d10
Listing updated: 3/2008 (software last revised 4/2007)
This R is a full-featured public domain software package developed by the University of Montreal. It is only available for Mac and VAX/VMS (click here to read more or download it). Version 4.0 is (as of February 2006) still under development by Philippe Casgrain and “developer” versions are being freely distributed to a wide number of sites.
Current Version: 3.192 / requires OS 10.7-10.11
Older versions: 680x0; PowerPC; OS X (Universal Binary); Windows and DOS
Listing updated: 12/2016 (program updated March 2014; web site updated in 2016)
G*Power was developed by Axel Buchner to provide power analyses for the most common statistical tests in behavioral research: t-tests, F-tests (including ANOVA, regression, etc.), and Chi-squared tests. G*Power computes power values for sample sizes, effect sizes, and alpha levels; sample sizes for given effect sizes, alpha levels, and power values; and alpha and beta values for given sample sizes, effect sizes, and beta/alpha ratios.
gretl can do general statistical routines and many specialized ones; it is in our “special purpose and general math programs” page.
Configurations: UNIX, OS X; since you compile it, Universal
Current version: 4.59
Program updated: 1-1-13
Listing updated: 7-7-13
A collection of command-line tools that run on all Unix-like systems, including Mac OS X. See gmt.soest.hawaii.edu for details. Many of the main developers (including me) use Mac OS X. (Description by Paul Wessel)
Configurations: PPC (older versions), 10.5+ (current)
Current Version: 2.20.3
Listing updated: 7-7-13
Graphviz is the AT&T open source drawing package. The Mac OS X version and the overall project have their own web sites. The OS X version now uses the Aqua user interface. Prepare for a steep learning curve but it may be worth it if you have graphs you do frequently; not what I'd suggest for the occasional one-off though.
Configurations: PPC (older versions), Intel (current)
Current Version: 4.6.3
Listing updated: 7-7-13
Program updated: 4-18-13
gnuplot is open source scientific plotting software. It is available online from many sources
Current Version: 2.2.1
Listing updated: 1-2-2017
StatCrunch is a freely available for web-based use, currently without advertisements, with a $5 per user fee for use on your own server, or $5/six months. It has the usual range of basic statistics, from t-tests to regression to ANOVA and nonparametric tests, with a wide range of graphs also available, and works from Excel or text files. StatCrunch will also store your data within reason. For those with low budgets or infrequent needs, StatCrunch's fairly easy to use interface and price are extremely attractive (it also makes sharing data easy).
Free - open source - for Mac OS X
Current version: 1.2.1
Report updated: 7/2013
Matplotlib is a pure python plotting library with the goal of making publication quality plots using a syntax familiar to matlab users. The library uses Numeric for handling large data sets and supports a variety of output backend.
On August 28 2012, John D. Hunter, the creator of matplotlib, died from complications arising from cancer treatment, after a brief but intense battle with this terrible illness. Please consider making a donation to the John Hunter Memorial Fund.
SciPy is a library of scientific tools for Python which supplements the Numeric module. SciPy includes modules for graphics and plotting, optimization, integration, special functions, signal and image processing, genetic algorithms, ODE solvers, and others.
May be compiled from source code for OS X, Linux, etc
Latest version: 7.1
Listing updated 1/2017
The Visualization ToolKit (VTK) is a system for 3D computer graphics, image processing, and visualization with several interface layers. In VTK applications can be written directly in C++, Tcl, Java, or Python.
“VTK supports a wide variety of visualization algorithms including scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques like implicit modelling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation. Moreover, we have directly integrated dozens of imaging algorithms into the system so you can mix 2D imaging / 3D graphics algorithms and data.”
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