snpar-package: Supplementary Non-parametric Statistics Methods

Description

Provide supplementary non-parametric statistics methods to perform several non-parametric tests based on rank score, estimate kernel probability density and cumulative distribution function, and fit kernel regression.

Arguments

Details

Package:

snpar

Type:

Package

Version:

1.0

Date:

2014-08-12

License:

GPL (>= 2)

This package contains several supplementary non-parametric statistics methods, including one- or two-sample quantile test and normal score test as well as multiple-sample normal score test, Cox-Stuart trend test, runs test for randomness, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirov test.

For a complete list of functions, use library(help = snpar).

References

Abdi, H. (2007). Bonferroni and Sidak corrections for multiple comparisons. In Salkind, N. J. Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage.

Conover, W. J. (1999). Practical Nonparameteric Statistics (Third Edition ed.). Wiley. pp. 396-406.

D.R. Cox and A. Stuart (1955). Some quick sign tests for trend in location and dispersion. Biometrika, Vol. 42, pp. 80-95. Fan, I. Gijbels (1996). Local Polynomial Modeling and its Applications. Chapman & Hall, London. Li, Qi; Racine, Jeffrey S. (2007). Nonparametric Econometrics: Theory and Practice. Princeton University Press. ISBN 0-691-12161-3. Nadaraya, E. A. (1964). On Estimating Regression. Theory of Probability and its Applications 9(1): 141-2. Wald, A. and Wolfowitz, J. (1940). On a test whether two samples are from the same population. Ann. Math Statist. 11, 147-162. Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.

Wang, J., Cheng, F. and Yang, L. (2013). Smooth simultaneous confidence bands for cumulative distribution functions. Journal of Nonparametric Statistics. 25, 395-407.

Wu, X. and Zhao, B. (2013). Nonparametric Statistics (Fourth Edition ed). China Statistics Press.