or in a Jupyter Notebook with an
Octave kernel 
. Note that
the first time you use statistics-resampling online in Octave with
Binder it will take a few minutes to build a docker image.Andrew Penn (andy.c.penn@gmail.com)
Andrew Penn
(More contributors are welcome!)
If you use this package, please include the following citation(s):
(Note that package versions 5.4.3 and below were named the ‘statistics-bootstrap’ package. The ‘statistics-resampling’ package is a more developed version of the older ‘iboot’ package).
The statistics-resampling package is an Octave package and Matlab toolbox that can be used to perform a wide variety of statistics tasks 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 calculate p-values for null hypothesis significance tests. Variations of the resampling methods are included that improve the accuracy of the statistics for small samples and samples with complex dependence structures.
Try out the statistics-resampling package online in Matlab or in a Jupyter Notebook with an
Octave kernel . Note that
the first time you use statistics-resampling online in Octave with
Binder it will take a few minutes to build a docker image.
(Collaborative) student projects in GNU Octave can use the statistics-resampling package at Octave-Online. Doing so requires users to download the latest release of the Source code (tar.gz) from here and follow steps 2-5 of these instructions.
Users who prefer Jupyter and have a workflow that is collaborative and/or crosses over multiple programming languages may find it more convenient to install and use the statistics-resampling package at COCALC. The approach described above (for Octave-Online) also applies to installing the statistics-resampling package via a Jupyter Notebook with an Octave kernel at COCALC.
Follow the links in the ‘Quick start’ section below to obtain some examples of data and code to try out with the package.
Users with greater computational demands may want to consider installing and running the statistics-resampling package offline. Installation of the statistics-resampling package has some software requirements. The core functions in this package require, and are known to be compatible with, GNU Octave (version >= 4.4.0) and Matlab (version >= R2007a 7.4.0). Some optional features of this package have further dependencies:
boot1way
requires the Statistics package (in Octave) or the Statistics and
Machine Learning Toolbox (in Matlab).To install (or test) the statistics-resampling package in your computer at a location of your choice, for either Matlab or Octave, follow these steps:
make to compile the MEX files from source (or use
the precompiled binaries if available. If suitable precompiled binaries
are not available for your platform, then Matlab/Octave will need access
to a C++11 compiler. Note that if you skip the make step, then the
package functions will still work, but some will run slower. This step
is interactive so check the command window.)install. The package will load now (and
automatically in the future) when you start Octave/Matlab.If you want or need to uninstall the package, change directory (cd) into the package folder and type uninstall.
Alternatively, Octave users can install the latest release of the package just like any other Octave package by typing:
pkg install -forge statistics-resampling
Or for the most recent developmental version of the package:
pkg install "https://github.com/gnu-octave/statistics-resampling/archive/refs/heads/master.zip"
The package can then be loaded on demand in Octave with the following commmand:
pkg load statistics-resampling
(Note that this isn’t necessary if you used the local installation instructions first described in this section)
Alternatively, MATLAB users can conveniently install the package functions as a toolbox by double-clicking the ‘statistics-resampling.mltbx’ file in the matlab subdirectory. The toolbox installed in this way can be disabled or uninstalled via MATLAB’s Add-On manager. MEX files are included with the toolbox installation for Windows (32- or 64-bit), MacOS (Intel or Apple Silicon 64-bit) and Linux (64-bit).
All help and demos are documented on the ‘Function Reference’ page in the manual. If you do not see the navigation pane on the manual web pages, please enable javascript in your browser. If you need further help with using any of the functions in this package, please post your questions on the discussions page.
Function help can also be requested directly from the Octave/MATLAB
command prompt, by typing help function-name - substituting
in the actual function name.
In Octave only, you can get a basic overview of the package and it’s
functions by typing:
pkg describe -verbose statistics-resampling, or request
demonstrations of function usage by typing
demo function-name. Users can also request help with using
functions and programming in Octave at the discourse group.
TIPS: Document and publish your analysis project using statistics-resampling functions in a Jupyter Notebook (with an Octave kernel) by forking and customizing the statistics-resampling-online GitHub repository. With Jupyter Lab, you can also integrate use of the statistics-resampling package into your analysis workflow along with other programming languages (e.g. Python, R or Julia etc.) by installing and configuring different kernels.
Below are links to demonstrations of how the bootstrap or randomization functions from this package can be used to perform variants of some commonly used statistical tests, but without the Normality assumption:
t-test to compare two independent samples (assuming equal variances)
t-test to compare two independent samples (assuming equal variances) (but also robust to outliers)
t-test to compare two independent samples (but also robust to heteroscedasticity, i.e. unequal variance)
Nested t-test to compare two independent samples. (See example listed below for nested one-way ANOVA)
One-way ANOVA to compare two or more independent samples (but also robust to heteroscedasticity)
Nested one-way ANOVA to compare two or more independent samples (but also robust to heteroscedasticity and grouping of observations)
Balanced two-way factorial ANOVA (but also robust to heteroscedasticity)
Unbalanced two-way factorial ANOVA (but also robust to heteroscedasticity)
Simple linear regression (but also robust to heteroscedasticity)
One-way ANCOVA (but also robust to heteroscedasticity)
Statistically evaluate the number of modes (i.e. peaks) in a distribution
Correcting sample size calculations for a two-sample test with nested design
For an example of how to import (larger) data sets from a human-readable text file, like a comma-separated-value (CSV) file, see the last demonstration listed on this page
If you find bugs or have any suggestions, please raise an issue on GitHub here. If you have any problems specifically with Binder for statistics-resampling online, please raise an issue on GitHub here. Please make sure that, when reporting a bug, you provide as much information as possible for other users to be able to replicate it.
Package: statistics-resampling