Tutorials

Setup
           Update to current version
           Setting default preferences

Importing Data
           Importing Excel, csv, text files
           Importing Access databases
           Importing large files

Analysis
        Summarize
            Summary statistics

        Transformation
            Transforms

        Association
            Correlation, Covariance, Distance
            Quasi-Diagonalization of a Correlation Matrix

        Independence (hypothesis testing)
           2-Sample T-test, Mann-Whitney, and 2 x c Chi-Squared tests
           k-Sample ANOVA, Kruskal-Wallis , and k x c Chi-Squared tests
           Paired tests (t-test, Wilcoxon signed rank)
           Tests for two independent proportions (p1,p2)
           McNemar's test

        Dependency (Regression)
           Multiple Linear (Y)
           Multivariate Linear (Y1,Y2,Y3,...)
           Binary Logistic (Y=0,1)
           Polytomous (Multinomial) Logistic Regression (Y=1,2,3,...)
           Poisson Regression (Multiplicative, relative risk model)
           Poisson Regression (Additive, absolute risk model)
           Poisson Regression (Geometric mixture model)
           Longitudinal Regression (GEE)

        Boosted (Regularized Regression)
         Boosted Multiple Linear Regression(Y)
           Boosted Multivariate Linear Regression(Y1,Y2,Y3,...)
           Boosted Logistic Regression (Binary and Polytomous)
           Boosted Poisson Regression
           Boosted Cox Proportional Hazards Regression

        Survival Analysis
           Kaplan-Meier group test
           Cox proportional hazards regression

        Text Mining
           Multiple text-file input
             Concept clusters via Word Frequency, Stopping, Stemming
             Sentiment mining via Word Frequency, Stopping, Stemming
             N-grams
           DataGrid input
            One record per document: Concept cluster via Stopping, Stemming
            One record per document: Sentiment mining via Word Frequency, Stopping, Stemming
            One record per document: N-grams
            Multiple records per document: Concept cluster via Stopping, Stepping
           Download PubMed abstracts

        Class Discovery (Pattern recognition & dimensional reduction)
          Run all methods
          Cluster Validity (Automated search for optimal #clusters)
          CKM - Crisp K-means
          FKM - Fuzzy K-means
          PSO - Particle swarm optimization
          SOM - Self organizing maps
          UNG - Unsupervised neural gas
          GMM - Gaussian mixture models
          URF - Unsupervised random forests
          PCA - Principal components analysis
          KDPCA - Kernel distance PCA
          KGPCA - Kernel Gaussian PCA
          KTPCA - Kernel Tanimoto PCA
          UANN - Artificial neural network
          t-SNE - Stochastic neighbor embedding
          DM - Diffusion maps
          LLE - Local linear embedding
          LEM - Laplacian eigenmaps
          LPP - Locally preserving projections
          SAMM - Sammon mapping
          NMF - Non-negative matrix factorization
          CMDS - Classic multidimensional scaling
          NMMDS - Non-metric multidimensional scaling
          HCA - Hierarchical cluster analsysis

        Feature Selection

        Class Prediction
           Accuracy Run of all Cross-Validation methods (all classifiers)
           Bootstrap Bias Run of 0.632 Accuracy (all classifiers)
           ROC-AUC Run for All Classifiers
           LREG - Linear regression
           DTC - Decision tree classification
           SRF - Random Forests
           KNN - K-nearest neighbor
           NBC - Naive Bayes classifier
           LDA - Linear discriminant analysis
           QDA - Quadratic discriminant analysis
           FDA - Fisher's discriminant analysis
           LVQ - Learning vector quantization
           PLOG - Polytomous logistic regression
           SVMGA - Support vector machines (gradient ascent)
           SVMLS - Support vector machines (least squares)
           SANN - Artificial neural network
           KREG - Kernel regression
           PSO - Particle swarm optimization
           SNG - Supervised neural gas
           MOE - Mixture of Experts

        Simulation
           Probability distributions
              Read in data, fit probability distributions
           Monte Carlo uncertainty analysis
              Generate new simulated correlated data - manual parameter entry
              Read in Cost Analysis Data - Perform Simulations
              Read in data, apply IC method, and couple with input marginal distributions
              Read in data, apply PQH method, and couple with input marginal distributions
              Read in data, fit probability distributions, apply IC method, and couple with simulated marginal distributions
              Read in data, fit probability distributions, apply PQH method, and couple with simulated marginal distributions