Hi,
My name is Marios and I have very good academic background as well as I have worked as modeling analyst in big projects thus I have experience with prediction and optimization algorithms. Recently (before 5 months) , I started learning JAVA and I have made my life much more simple by using Java and Common math rather than depending on the common packages (SAS SPSS etc). Obviously, I owe common math a lot. I have noticed that the site does not have logistic regression and probit regression, very commonly used in classification problems. Additionally, The math package does not provide a way to assess Tolerance (or VIF), very commonly used to avoid multi-colinearity issues and singular matrices in optimization algorithms, prior to running them. I am willing to provide complete Logistic and Probit regression algorithms, optimizable by newton Raphson optimization maximum-likelihood method , in a very programmatically easy way (e.g regression(double matrix [][], double Target[], String Constant, double precision, double tolerance) , with academic references and very quick (3 secs for 60k set), with getter methods for all the common statistics such as null Deviance, Deviance, AIC, BIC, Chi-square f the model, betas, Wald statistics and p values, Cox_snell R square, Nagelkerke’s R-Square, Pseudo_r2, residuals, probabilities, classification matrix. I have also included steps for checking tolerance so that we avoid cases that fail to converge. Generally the algorithm is not very expensive for the RAM (because I have approximated the Hessian Matrix) and the only external jar that I use is common math for multiplications of matrices. Regards
