Using cabinetry for a custom statistical model

[bravina]

I'm looking for a fitting framework in python that would satisfy the needs of my analysis, without having to reinvent the wheel e.g. with respect to nuisance parameters and profiling. Can I do (and how?) the following in cabinetry:

1. read in a series of ROOT histograms and interpolate (e.g. linearly) between them to define the predictions of my model via morphing
2. write a likelihood function that is essentially a chi-squared with Gaussian constraints on the systematic nuisance parameters
3. also allow for non-Gaussian constraints (e.g. Student's t-distribution)
4. pass in the data statistical covariance matrix

Thanks!