Features

DESIGN OF EXPERIMENTS
Full factorial
Fractional factorial
Central composite
Plackett-Burman
Mixture design
Desirability Function

PATTERN RECOGNITION
PCA
HCA
PCA/HCA

MULTIVARIATE CALIBRATION
MLR
PCR
PLS

CLASSIFICATION
LDA
PCR-DA
PLS-DA

 TOOLS
Leave-one-out cross-validation‎
Test set selection
Data plot
Build dataset from .bmp molecules for MIA-QSAR
Variable Importance in Projection (VIP)

PRE-PROCESSING
Mean center
Autoscale
Smoothing/derivative
Normalization
Multiplicative scatter correction
Spectral (absorbance/transmittance)
Standard Normal Variate

IMPORT DATA
.txt
.dat
.csv
.bmp