Preview of Statistical Inference Package SIP Version 2.0

Esa Uusipaikka

University of Turku

Abstract

Statistical Inference Package SIP is a Mathematica package for doing likelihood-based statistical inference. It contains a large set of functions for defining statistical models for empirical data. These include sampling model, general linear regression, nonlinear regression, logistic regression, Poisson regression, generalized linear regression, multinomial regression, multivariate linear regression, mixture model, mixed model, Markov chain model, Hidden Markov model, etc. Using these models with data SIP provides the user methods for calculation profile likelihood-based confidence intervals for any smooth real-valued function of the parameters and likelihood ratio tests for statistical hypothesis defined by any smooth real-valued functions of the parameters.

Version 2.0 of Statistical Inference Package SIP includes following new features.
	1) model formula,
	2) higher order correction for confidence intervals,
	3) bootstrap correction for confidence intervals and likelihood ratio tests,
	4) simultaneous confidence intervals,
	5) calculation and plotting residuals for regression model,
	6) various random effect regression models,
	7) proportional odds and hazard (Cox) regression models,
	8) data management,
	9) view of models and results,
	10) plot of data and results,
	11) etc.