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# Features

## Analysis of Variance

• ANOVA for balanced data

• general linear models

• unbalanced data

• analysis of covariance, response-surface models, weighted regression, polynomial regression, MANOVA, repeated measurements analysis

• least squares means

• random effects

• estimate linear functions of the parameters

• test linear functions of the parameters

• multiple comparison of means

• homogeneity of variance testing

• mixed linear models

• fixed and random effects

• REML, maximum likelihood, and MIVQUE0 estimation methods

• least-squares means and differences

• sampling-based Bayesian analysis

• many covariance structures, some of which are compound symmetry, unstructured, AR(1), Toeplitz, heterogeneous AR(1), and Huynh-Feldt

• multiple comparison of least-squares means

• repeated measurements analysis

• nonlinear mixed models

• variance components

• nested models

• lattice designs

## Regression Analysis

• ridge regression

• linear regression

• nine model-selection techniques including backwards, forwards, stepwise, and those based on R-squared

• diagnostics

• hypothesis tests

• partial regression leverage plots

• outputs predicted values and residuals

• graphics device plots

• response surface regression

• nonlinear regression

• derivative-free

• steepest-descent, Newton, modified Gauss-Newton, Marquardt and DUD methods

• linear models with optimal nonlinear transformation

• partial least squares

## Categorical Data Analysis

• weighted least squares analysis

• loglinear models

• generalized estimating equations

• Mantel-Haenszel Methods

• Fisher's exact test

• r x c exact tests

• probit analysis

• logistic regression

• various model-selection methods

• proportional odds model for ordinal response

• regression diagnostics

• conditional logistic model

• roc curves

• discrete choice models

• multinomial logit models

• generalized linear model

• probability distributions include normal, binomial, Poisson, negative binomial, gamma, and inverse Gaussian

• link functions include logit, probit, identity, complementary log-log, log, and power with lamda=value

• profile likelihood-based confidence intervals

• likelihood ratio statistics for contrasts

• user-defined link functions and probability distributions

## Multivariate Analysis

• principal components

• canonical correlation

• discriminant analysis

• structural equation modeling and path analysis

• general COSAN model

• optimization methods include Levenberg-Marquart algorithm, ridge-stabilized Newton-Raphson, quasi-Newton, and conjugate gradient algorithms

• estimation methods include maximum likelihood, least squares, generalized least squares, weighted least squares, and diagonally weighted least squares

• equality and inequality constraints

• multivariate one-way ANOVA model, discrete or continuous variables

• linear contrasts to compare proportions/means

• adjustments include bootstrap and permutation resampling

• multidimensional scaling models

• simple Euclidean and weighted Euclidean models

• ordinal, interval, ratio, or absolute levels of measurement

• fits distances, squared distances, log distances, or distances raised to any power

## Survival Analysis

• parametric models for failure-time data

• left, right, interval-censored

• model random disturbance with extreme value, normal, logistic, exponential, Weibull, log-normal, log-logistic, gamma distributions

• nonparametric methods

• Kaplan-Meier and lifetable estimates

• tests for homogeneity of survival distributions

• rank tests for association of response with covariates

• Cox regression (semi-parametric proportional hazards model)

• time-dependent covariates

• stratified analyses

• counting process formulation

• various model-selection methods

• four methods of handling ties

## Psychometric Analysis

• factor analysis

• simple and multiple correspondence analysis

• multidimensional scaling

• conjoint analysis

• principal components

• multidimensional preference analyses

• preference mapping

## Cluster Analysis

• hierarchically cluster data

• disjoint clustering of very large data sets

• approximate covariance estimation for clustering

• disjoint or hierarchical clustering based on correlation or covariance matrix

• clustering based on nonparametric density estimates

• numeric coordinates or distance data

• approximate significance tests for number of clusters

• hierarchical joins of nonsignificant clusters

## Nonparametric Analysis

• simple linear rank statistics based on Wilcoxon, median, Savage, and Van der Waerden scores

• exact p-values for simple linear rank statistics

• tests for scale differences include Siegel-Tukey, Ansari-Bradley, Klotz, and Mood

• Kolmogorov-Smirnov statistic

• kernel density estimation

• loess regression

• thin-plate smoothing splines