Introduction

doe is a python package for the computation of (D-)optimal experimental designs. It uses opti for experiment specification and adding domain knowledge and formulaic.

Opti allows to define an arbitrary number of decision variables using Problem objects. These can take values corresponding to their type and domain, e.g.

• continuous: \(x_1 \in [0, 1]\)
• discrete: \(x_2 \in \{1, 2, 5, 7.5\}\)
• categorical: \(x_3 \in \{A, B, C\}\).

Warning

Discrete and categorical variables cannot currently be constrained.

Additionally, constraints on the values of the decision variables can be taken into account, e.g.

• linear equality: \(\sum x_i = 1\)
• linear inequality: \(2 x_1 \leq x_2\)
• non-linear equality: \(\sum x_i^2 = 1\)
• non-linear inequality: \(\sum x_i^2 \leq 1\)
• n-choose-k: only \(k\) out of \(n\) parameters can take non-zero values.

The model to be fitted can be specified using formulaic's Formula objects, strings following Wilkinson notation or - with the context of the problem specification - using certain keywords like "linear" or "fully-quadratic".