Parameter Identifiability with ModelingToolkit.jl

The problem of identifiability is ubiquitous among experimental scientific research. The core idea is in one's ability to recover parameter values from the inputs and outputs (measurements). If such recovery is theoretically possible then the model is called structurally identifiable. This problem is commonly solved via symbolic computation. In this project, the aim is to augment the existing ModelingToolkit.jl package with algorithms that solve the problem of structural local and global identifiability analyses.