Dew Lab Studio is a superset of our Delphi math product line at a reduced price. It includes MtxVec, DSP Master, Stats Master and Data Miner. The package price also includes latest Steema TeeChart license (a $599 value). The trial version, which includes all products is available within a single installer.

MtxVec is an object oriented numerical library for Delphi and .NET developers with complete matrix/vector arithmetic. It features a large set of vectorized mathemathical functions which cover complex numbers, sparse matrices, math parser, probabilities, optimization unit, SVD, QR, LQ, and LU solvers, special functions, and more. All applications based on this library take advantage of CPU-specific code optimization and symmetric multiprocessing. Efficient memory and CPU cache management further enhance its performance.

This signal processing package for MtxVec features Infinite Impulse Response (IIR) filter designers: Butterworth, Chebyshev, elliptic, Bessel, and Finite Impulse Response (FIR) designers: window, Remez exchange. Included are also over 40 components such as: frequency analyzer, cross spectral analyzer, higher order spectral analyzer, signal generator, arbitrary rate convertors, streaming pipelines, audio playback and recording, ASIO audio drivers, fractional delay filters, signal forecasting, read/write audio file support and many other features.

The Stats Master statistical package for Delphi includes: 21 different distributions (PDF, CDF and inverse CDF function), mean and variance for all 21 distributions, random generators for 18 distributions, parameter estimate for beta, binomial, exponential, gamma, geometric, normal, Poisson, continuous uniform and Weibull distributions, histograms, ogives nth-Moment, percentile, range, Interquertile Range IQR, mean, harmonic mean, goodness-of-fit tests and more.

Data miner is a set of components for classification for Borland Delphi and C++Builder written in 100% VCL. The algorithms included cover: KNN and Naive Bayes plus a third completely new algorithm named Linear Classifier. The algorithms can work on real and discrete data and can be connected to a TDataSet descendant. They appropriately handle missing data and are all capable of incremental learning.