NPStat is hosted by Hepforge, IPPP Durham

NPStat — Non-parametric Statistical Modeling and Analysis

The NPStat package is designed to address the problem of non-parametric statistical modeling of probability densities and regression surfaces. This type of modeling becomes very useful when there is little prior information available to justify an assumption that the data belongs to a certain parametric family of distributions or curves. The main intended application is fast and detailed modeling of the response and transfer functions of particle detectors, but the package is sufficiently general and can be used for solving a variety of statistical analysis problems from other areas. Both univariate and multivariate models are supported, and a number of original algorithms are implemented. Package capabilities include

  • Calculation of descriptive sample statistics
  • Arbitrary-dimensional histogramming
  • Parametric, semi-parametric and non-parametric density modeling
  • Generation of pseudo- and quasi-random numbers according to various density models
  • Non-parametric density interpolation (morphing), including multivariate densities
  • Non-parametric copula modeling and copula-based density interpolation
  • Fast kernel density estimation (KDE) via DFFT
  • Density estimation by local orthogonal polynomial expansion (LOrPE)
  • Density estimation by the nearest neighbors method
  • Local regression techniques: local polynomial least squares, iterative local least trimmed squares, local logistic regression, local quantile regression with and without censoring
  • Expectation-maximization unfolding with smoothing
More information about NPStat statistical techniques and their implementation is available from this document.

The C++ API (doxygen)

Information about the python API

Download NPStat

Contact: Igor Volobouev