MacAnova – statistical analysis and matrix algebra software

Last Updated on July 11, 2021

MacAnova is a free, open source, interactive statistical analysis and matrix algebra multi-platform program. MacAnova has many capabilities, including the design of experiments.

While MacAnova is not as feature-laden as R, it has considerable strengths including the 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. It shares some functions of R.

Features include:

• Variables and operations:
  • Named real, logical and character vectors, matrices and arrays, plus structures and graph variables.
• • Coordinate labels for vectors, matrices and arrays.
• • Usual arithmetic (+, -, *, /, ^ or **) plus modular division (%%) and bit manipulation (%&, %|, %^, %!).
• • Most operators and many functions work with structures, allowing analysis of non-rectangular data sets.
• • Descriptive notes can be attached to all variables.
• Help:
  • On-line usage summaries and complete help for over 550 topics in 8 automatically searched files.
• • HTML help you can read in your favorite web browser.
• Descriptive statistics
  • Means, variances, medians, quartiles, extremes, skewness, kurtosis.
• • Cross tabs and cell means, variances, standard deviations and extremes, plus cell covariance matrices for multivariate data.
• Linear and generalized linear models:
  • A linear model grammar with multiple error terms and shortcuts for polynomial and periodic regressions.
• • Up to 96 variables, up to 31 of which can be categorical factors.
• • “On the fly” transformations of response and predictor variables.
• • ANOVA, MANOVA and regression with optional weights.
• • Robust ANOVA and regression.
• • Logistic, probit and Poisson regression.
• • Iterative proportional fitting.
• • Model coefficients, standard errors, contrasts.
• • Residual plots and macros to summarize residuals.
• • Branch and bound determination of best subset regression with ability to force in a subset and save the models selected.
• • Macros for step-wise regression.
• • Power and sample size functions for CRD and RBD.
• • t-tests, confidence intervals.
• • Expected mean squares.
• • Macro for non-linear least squares.
• Matrix algebra:
  • Matrix multiplication (operators %*%, %c% and %C%) and inversion.
• • Linear equation solution (operators %/% and %\%).
• • Eigenvalues and eigenvectors and relative eigenvalues and eigenvectors.
• • Cholesky, QR and SVD decompositions.
• • Beaton sweep operator.
• • Trace, diagonal, determinant, outer products, and other matrix and vector manipulations.
• • Permutation of dimensions of matrices and arrays.
• • Other operations such as Kronecker products and Moore-Penrose inverses are provided as macros.
• Time series
  • Fast Fourier transforms (maximum prime factor of length is 29).
• • Convolution and sums of lagged products.
• • Forward and backward autoregression and moving average operators.
• • Yule-Walker solver and its inverse.
• • ACF to partial ACF and its inverse.
• • Spectrum and cross-spectrum analysis, including multi-taper estimation.
• • Macros for least squares and maximum likelihood estimation of ARIMA models, including seasonal models.
• • Macros for Hannan-Rissanen and innovations estimation of ARIMA models.
• • Macros for computing approximate covariances and variances of autocorrelations using Bartlett’s formula.
• • Macros to compute the autocovariance function and the spectrum corresponding to an ARMA model.
• • Time series plots.
• • Frequency function plots.
• Multivariate analysis:
  • MANOVA.
• • Hierarchical cluster analysis.
• • K-means cluster analysis.
• • Macros related to discrimination and factor analysis, including stepwise discriminant analysis.
• • Macros for ULS, GLS and ML factor extraction.
• • Varimax, quartimax, equimax and orthomax factor rotation.
• Design of experiments:
  • Confounding 2 and 3 series factorials.
• • Finding aliases in 2 and 3 series fractional factorials.
• • Choosing generators and design points in 2 series fractional factorials.
• • Canonical analysis of 2nd order response surface.
  • Constrained maximization of quadratic functions.
• • Recovery of interblock information in incomplete block designs.
• • REML analysis of unbalanced models with fixed and random terms.
• • “ANOVA” estimates of random effect variances in mixed effects.
• • Expectation of mean squares in mixed models analysis of variance.
• Random numbers and probabilities:
  • Uniform, normal, Poisson and binomial pseudo-random number generators.
• • Cumulative and inverse normal, chisquare, F, t, beta, gamma.
• • Cumulative and inverse noncentral chisquare.
• • Cumulative non-central F, beta and t.
• • Cumulative poisson and binomial.
• • Cumulative and inverse Studentized range.
• • Cumulative and inverse Dunnett’s t.
• Graphical features:
  • Scatter plots, including several y’s vs one x.
• • Line and impulse plots.
• • Box plots, histograms and stem and leaf displays.
• • Interaction plots.
• • Panel graphs, rectangular arrays of small graphs, including scatterplot matrix.
• • Contour plots with interactively labelled contours.
• • Stepwise construction of graphs.
• • Replotting with changed labels, bounds and tick marks.
• • Mouse specification of locations for points and lines on a graph.
• • GRAPH variables encapsulating all the information in a graph.
• • Graphs are saved when you save your workspace and redrawn when you restore the workspace.
• Transformations:
  • max, min, sum, prod, all operating on columns of matrices.
• • Usual transcendentals.
• • Rational functions (ratios of polynomials).
• • Ordering, ranking, sorting.
• Importing and exporting data:
  • Direct importing of data from spreadsheets using the clipboard.
• • Direct exporting of data and results to other programs using the clipboard.
• • Reading and writing named data sets from and to plain text files.
• • Reading unstructured data together with variable names from plain text files.
• Programmability:
  • Macros used just like functions.
• • Many built-in macros plus 8 files of loadable macros (general, time series, ARIMA modeling, design of experiments, regression, multivariate analysis, graphics, and mathematics).
• • Functions for automatic checking of macro arguments.
• • Automatic searching for macros that have not yet been loaded.
• • For and while loops, if, else, elseif, break, next, return.
• User functions:
  • Dynamic loading and execution of user compiled code with callbacks to MacAnova functions.

Website: www.stat.umn.edu/macanova
Support: Documentation
Developer: Gary W. Oehlert, Christopher Binghams
License: GNU GPL v2

MacAnova is written in C. Learn C with our recommended free books and free tutorials.

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