About 30 years or so ago a modeling framework known as Structural Equation Models(SEM) became known for its ability to generalize and model observable and unobservable(latent) variables and to include specifications about multiple sources of variation. Early on Mplus was known for its treatment of models involving latent variables. Over the years Mplus has broaden its scope of features to more fully address mixed variable settings, advance simulation options, censoring/survival models, nonlinear growth and MultiLevel models. Some of the functionality in Mplus can be found in Spss’s Amos product and SAS’s Proc Calis feature, for example. Complex models such as these present unique chanllanges to the inference process. Both Mplus and Amos address this issue with optional bootstrapping of either residuals or observations. Whereas, R ‘s SEM package will bootstrap observations to estimate parameter standard errors. Additional information can be found at: http://www.statmodel.com/features.shtml
JMP use to be a SAS Institute data analysis product for the Apple platform. This allowed SAS to leverage its vast array of statistical software available across many other platforms. Over the years the vendor added Windows/Intel machines. Now a Linux version is available. The latest version has grown to include many options, thus making it a more mainstream statistical package. Besides the rock solid underlying routines provided by SAS, the GUI is perhaps its real strength thus making it a productive enviornment for interactive use. A free 30-day full feature version is now available, check: http://www.jmp.com
NAG is well known for providing numerical subroutines for scientific computing tasks for years. Statistical and visualization software components are also available. Recently an Excel add-in option providing 76 statistical functions is available for those that mosly use Excel as a statistical computing alternative to a larger statistics only package. Given NAG’s excellent record for reliability and accuracy, one might consider this as a superior alternative to MicroSoft’s add-in statistics option.
Additional info at:http://www.nag.com/stats/ae_soft.asp
The TISEAN software package is a collection of command line utilities addressing
methods of nonlinear time series analysis. These are based on the paradigm of deterministic chaos. A variety of algorithms for data representation, prediction, noise reduction, dimension and Lyapunov estimation, and nonlinearity testing are discussed with particular emphasis on issues of implementation and choice of parameters. Source code in C and Fortran is publicly available. Support for GnuPlot is provided.
TISEAN can be found at:
StatXact software is a suite of statistical tools that provide exact inference in the nonparametric statistical inference setting. StatXact has a large number of statistical procedures addressing one, two or K-sample problems, problems involving contingency tables, measures of association and StatXact addresses stratified settings as well. These tools provide assurance of p-values in small sample, unbalanced or missing sampled data settings. A related product, LogXact is their exact logistic regression tool for similar situations. Added recently are options for Penalized Maximum Likelihood Estimation and methods for dealing with missing categorical covariates in GLM settings using Logit, Probit, CLogLog, Poisson, and Normal links.
For additional info check out:
Change point problems can be found in many areas of science and engineering. The solutions vary according to the modeling setting. A search of the literature will reveal the large scope of solutions and settings. A shareware software option addressing the narrow areas of sequential processes, timeseries and control charts is Change-Point Analyze(CPA)
. CPA analyzes time ordered data to determine whether a change has taken place. It detects multiple changes and provides both confidence levels and confidence intervals for each change. Check out the 30 day demo at:
WiSP is an R library of functions designed as a teaching tool to illustrate methods used to estimate the abundance of closed animal populations. It enables users to generate animal populations having realistically complex spatial and individual characteristics, to generate survey designs for a variety of survey techniques, to survey the populations and to estimate abundance. WiSP can be found at:
Missing data problems and variance estimation in complex surveys is a standing problem facing most large scale surveys. IVEware was developed by the Survey Methodology Program at the University of Michigan’s Survey Research Center, Institute for Social Research and is available to researchers without cost.
A SAS interfacing version(requiring SAS) and stand alone Windows and Linux versions are available.
Additional information is available at: http://www.isr.umich.edu/src/smp/ive/
XploRe is a combination of classical and modern statistical procedures, in conjunction with sophisticated, interactive graphics. XploRe is the basis for statistical analysis, research, and teaching. Its purpose lies in the exploration and analysis of data, as well as in the development of new techniques. In addition, XploRe is a high level object-oriented programming language.
XploRe is a complete statistical programming package, including a great variety of methods such as: generalized linear models and generalized partial linear models, nonparametric methods such as kernel estimation and smoothing, spline smoothing, single index models, generalized additive models, finanical option pricing, stock simulation, nonlinear time series analysis, and modern regression techniques with wavelets and neural networks.
Both commercial and free academic versions are available. Additional information is available at:
High dimensional data presents potential problems to standard modeling and estimation tasks commonly confronted by the data analyst. Most methods in most statistical packages do not address these issues. Robust estimation theory over the last 40 years has changed the landscape of ideas around what constitutes good practice and procedure. The goal of robust statistics is to develop data analytical methods which are resistant to outlying observations conditional on the model at hand and for a specified influence function. Such methods are able to discriminate outliers from model consistant data. LIBRA is an interesting collection of free Matlab programs designed for this very task. Further details can be found at: