This includes models that combine some quadratic aspect & a cross term.
Remove random test

[contrasting] fix polynomial_margin_model.py creation. extend the form_dict for spatio temporal models.

[exceeding] add full diagnositic plot (qqplot & intensity plots) for gumbel stationary & add gumbel gof tests from r package

[exceeding] add full diagnositic plot (qqplot & intensity plots) for gumbel stationary & add gumbel gof tests from r package

[refactor] refactor main fitting function: by default, we fit without starting value, then we try several time with the start value

[quantile regression project] add test exp simulations (stationary and non stationary) for models on annual maxima. Prepare the code to fit with non stationary exponential models

[Confidence Interval] use fix r.ci.fevd.mle_fixed for the Gumbel case in the Python code. fix the R code one more time for the gradient

[TEMPORAL LINEAR MARGIN][BAYESIAN ESTIMATION] add fevdPriorCustom as prior for the fit. update test accordingly. add non stationary version of fevD both in R and also in python. add test for that in python. modify upper and bottom quantile for eurocode

[EUROCODE][DRAWING PER REGION] modify test region eurocode. add test_ gev temporal bayesian. add stationary version for the Bayesian. refactor utils at the root, to root_uils to avoid weird import issues.

[EXTREME ESTIMATOR] refactor margin_fits folder. extreme estimator folder. and extreme_models folder

[CHANGE POINT TEST] improve management of crash mode, to avoid crashing all the run (issues with test with scale parameter)

[EXTREME ESTIMATOR] add max iteration argument for any R fit. also add some verbose for trend tests experiments.

[EXTREME ESTIMATOR][MARGIN MODEL] add ismev gev fit. and refactor. add result_from_fit abstract class.

[EXTREME ESTIMATOR][MARGIN MODEL] add stationary/non-stationary margin model. fix margin formula (in the temporal case when we want a dependency on a single parameter for instance). add margin function with starting point

[EXTREME FIT] After any Spatial Extreme fit, create an object ResultFromFit, refactor code accordingly

[EXTREME ESTIMATOR][EXTREME MODEL] add warning when maximum absolute value of data is too high in safe_run_r_estimator. add test.