Function Reference
The statistics-resampling package is an Octave package and Matlab toolbox that can be used to overcome a wide variety of statistics problems using non-parametric resampling methods. In particular, the functions included can be used to estimate bias, uncertainty (standard errors and confidence intervals), prediction error, and test hypotheses (p-values). Variations of the resampling methods are included that improve the accuracy of the statistics for small samples and samples with complex dependence structures.
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Main functions
Cloned Matlab functions
Utility Functions
Parameter functions
Performs one or two levels of bootknife resampling and calculates bootstrap bias, standard errors and confidence intervals.
Performs balanced bootstrap (or bootknife) resampling of clustered data and calculates bootstrap bias, standard errors and confidence intervals.
Performs wild bootstrap and calculates bootstrap-t confidence intervals and p-values for the mean, or the regression coefficients from a linear model.
Performs Bayesian nonparametric bootstrap and calculates posterior statistics for the mean, or regression coefficients from a linear model.
Performs a permutation or randomization test to compare the distributions of two independent or paired data samples.
Uses bootstrap to calculate confidence intervals (and p-values) for the regression coefficients from a linear model and perform N-way ANOVA.
Performs resampling under the null hypothesis and computes p-values for (multiple) comparisons among independent samples in a one-way layout.
Uses bootstrap to evaluate the likely number of real peaks (i.e. modes) in the distribution of a single set of data.
Balanced bootstrap resampling.
Performs single or double bootstrap (or bootknife) resampling and calculates confidence intervals.
This function returns resampled data or indices created by balanced bootstrap or bootknife resampling.
Computes the empirical cumulative distribution function (ECDF), accounting for the presence of ties.
Computes credible intervals from a vector of posterior data values, for example, those obtained by bayesian bootstrap.
Computes the design effect (DEFF), which can subsequently be used to correct sample size calculations using the 'sampszcalc' function.
Performs sample size calculations, with optional correction for the design effect deviating from unity.
Calculates a smoothed version of the median.
Calculates a smoothed version of the median absolute deviation (MAD).
Vectorized function for computing Pearson's correlation coefficient (RHO) between the respective columns in two equal-sized matrices.
Package: statistics-resampling