equadratures
v10 will be released early next year! Some of the planned features are listed below.

Bayesian polynomial models

Polynomial priors and posteriors for experimental vs. computational misfits

Polynomial priors for multifidelity / multilevel models

Polynomial coregional models for correlating similar polynomials

Bayesian polynomial quadrature strategies


Logistic regression in
equadratures

Network polynomials

A network lassotype framework with simultaneous clustering and regression

Sensitivity analysis for each cluster

Subspacebased analysis

Both L_1 and L_2 norm based cluster distance metrics


A minimumcode
eqgo
class focused on endtoend results 
Orthogonal polynomial expansions’ root finding for speeding up optimisations

Adaptive quadrature rules for polynomial least squares

Addition / subtraction / multiplication of polynomials

Basic spline fitting functionality

Enhanced syntax for streamlining the code

Moving away from
param = Parameter(distribution='Normal', shape_parameter_A=2.5, shape_parameter_B=4.5, order=2)
to
param = Normal(mean=2.5, variance=4.5, order=2)
whilst maintaining backward compatibility. 
Having correlations defined as part of a parameter list class (that extends the default
list
class).
