Improvements for JuliaNLSolvers could be made in three parts: documentation, benchmarks and functionality.
NLsolve.jl only have example codes in their READMEs. Documentation for these projects will be good references for users. Beginner’s guide would dramatically reduce the learning curve for new users. Examples are also needed for
NLsolve.jl to show people the Julia “pipeline” in areas such as Machine Learning, Statistics and Economics. Meanwhile, codes in documentation and examples can be used for testing.
Benchmarks are essential to show the advantage of Julia and therefore may persuade outside users to switch. By comparing with
SciPy, it will also help guide development and find bugs.
LsqFit.jl is still on an early development stage and has large potential to improve. For example, allowing non-vectorized functions for
LsqFit.jl will help it apply to more problems.