* added `flat_growth_init()` function
* added validation for 'flat'
* changed `fit.prophet()` to handle `growth='flat'`
* added `trend='flat'` capabilities to `sample_predictive_trend()` and `fit.prophet()`
* updated STAN code to handle flat trend
* [Syntax fix] Removed unnecessary bracket
* updated documentation
* undid formatting that was accidentally applied by autoformatter
* undid more formatting that was accidentally applied by autoformatter
* added tests
* typo in `sample_predictive_trend()`
* updated notebook with example in R
* updated documentation
* update docstring with mdape in list for python perf metric func
* re-run python notebook cell to generate mdape in results table
* remove comment
Co-authored-by: Ben Letham <bletham@gmail.com>
* add test and initial function for mdape in R
* Add separate rolling_median_func and tests
* Modify rolling median function
* fix syntax in rolling median function
* sort by h
* R/diagnostics.R
* update .rd docs and notebook
* Add mdape to performance metrics params docstring
* Add test for custom cutoff cv
* implement custom cutoff logic in cv function
* add docstring
* add description in notebook and rebuild .Rd docs
* fix bug and add test case for period is NULL
* replace s.POSIXct set_date
* add implementation for constant trend
* force k and delta params to be 0s
* add tests and fix n_changepoints, changepoints_t to 0
* Add test for cv with constant trend
* Add docs and test for checking invalid input
* make changes to stan
* add transformed params block in stan and output flat trend vector
* correct syntax
* transformed params syntax
* Fix test and port changes to win stan file
* add test for flat trend function
* Add separate function for flat trend init
* fix test
* Add example of dask distributed parameter tuning to notebook diagnostics
* Rearrange description blocks and correct typos
* Add more informative explanations and tips in docs for tuning examples
* increase output df column width to see results
* add instructions for multiprocess and threaded
* reset index of hyperparameter tuning results df
* fix typos
* remove blank code cell
* grammar correction
* Add subsection
* Split different parameter optimsiation examples
* Add multiprocessing Pool and create function for single cutoff forecast
* add params to single forecast function
* Add iterable for input params for pool
* Add docstring for single cutoff forecast func
* Add check for multiprocessing in test_cross_validation
* check ofr is None and is True and add better description for multiprocess in docstring
* Raise error if wrong args chosen and add test
* fix conflicts
* Change arg to True/False, model.kwargs
* docstring units and few more fixes
* change to iterator and add back tqdm to for loop
* add option in diagnosics notebook about multiprocessing option
* add extra test for checking calls to single forecast func
