lind package

Subpackages

• lind.analysis package
  • Submodules
  • lind.analysis.freq module
  • lind.analysis.multiple_comparison_procedures module
  • Module contents
    • Analysis
• lind.design package
  • Subpackages
    • lind.design.randomization package
      • Submodules
      • lind.design.randomization.md5 module
      • Module contents
  • Submodules
  • lind.design.box_behnken module
  • lind.design.box_wilson module
  • lind.design.factorial module
  • lind.design.latin_hyper_cube module
  • lind.design.plackett_burman module
  • lind.design.taguchi module
    • Workflow Example
  • Module contents
    • Design
• lind.r_backends package
  • Subpackages
    • lind.r_backends.analysis package
      • Module contents
    • lind.r_backends.design package
      • Submodules
      • lind.r_backends.design.taguchi module
      • Module contents
  • Module contents

Submodules

lind.library module

TODO:

Module contents

Lind

Lind is a package for experimental design and analysis.

How to use the documentation

Documentation is available in two forms: docstrings provided with the code, and a loose standing reference guide, available from the documentation site https://james-montgomery.github.io/lind/build/html/index.html

We recommend exploring the docstrings using IPython, an advanced Python shell with TAB-completion and introspection capabilities. See below for further instructions.

The docstring examples assume that numpy has been imported as np::

>>> import lind as ld

Code snippets are indicated by three greater-than signs::

>>> x = 42
>>> x = x + 1

Use the built-in help function to view a function’s docstring::

>>> help(ld.design.design_full_factorial)

For contributors, we have a template docstring at the bottom of this file for reference. The style is a modified numpy style docstring. We have added sections for academic references and statistical notes.