Here are the tools available with KATE.  

Data extraction, transformation and visualization

• Data merge by keys - allows you to merge two sheets (contained in the same file) by up to three common key-columns..

• Scatterplot with labels - automatically adds labels to a scatterplot, allowing you to jigger the labels to avoid crowding

• 3D Scatterplot - simple tool to produce and rotate 3D scatterplots; all you need is a label and three coordinates for each data point.

• ExtracText - simple tool for extracting, mining and coding words from text documents

• WordMap – produces a word and a document map representing the frequency and affinity of words in a sample of text documents

Unsupervised learning

• K-means clustering - the good-old workhorse for classifying cases based on continuous data

• Latent Class Analysis - finite mixture modeling for categorical, ordinal and interval-scaled (i.e., Likert scale) data.

• Correspondence Analysis - space-reduction technique for categorical data more popular in Europe than in the US.

• Principal Components Analysis - another popular space-reduction technique, for continuous data.

• Dynamic Factor Analysis - similar to Principal Component Analysis, except that the factor scores represent smooth latent trends over time.

• Metric Multidimensional Scaling - a tool for unfolding a symmetric table of distances between cases into a multidimensional map.

Supervised Learning

• Stepwise Regression - a standard linear regression with stepwise selection of predictors.

• Logistic Regression - binary logistic regression.

• Local Geographic Regression - Estimates one regression for each data point, using data from its nearest-K neighbors (you must have geo-codes for each data point)

• Univariate PLS Regression - space reduction for a set of predictors while maximizing their fit to a continuous dependent variable.

• Multivariate PLS Regression - as extension of PLS Regression, for explaining multiple dependent variables using a set of predictors. 

• Mixture Logit - random-coefficients Multinomial Logit choice model, producing individual-level estimates for the response coefficients.

• Stochastic Frontier Regression - linear regression with asymmetric errors; the regression line is fitted around the top or bottom of the cloud of points.

• Qualitative Data Envelopment Analysis - very flexible DEA tool that allows for qualitative inputs and outputs.

• Sliced Average Variance Estimation - space-reduction for a set of predictors while maximizing the fit to a binary dependent variable.

• Predictive Stepwise Linear Regression - stepwise predictor selection in a Linear Regression to maximize predictive fit in hold-out samples, rather than calibration fit

• Predictive Stepwise Logistic Regression – stepwise predictor selection in a Binary Logistic Regression to maximize predictive fit in hold-out samples.