Add Stein-Fojo Model

[gowerc]

Closes #171

[danielinteractive]

Thanks @gowerc , any chance we can exploit the relationship between SF and GSF? E.g. SF being a special case of GSF?

[gowerc]

@danielinteractive - I mean almost certainly yes, but when I first attempted to go down that route it just felt like the dynamic code (particularly in the Stan + Jinja side of things) was more of a hassle than just the simple c&p + name changes.

[github-actions]

Unit Tests Summary

  1 files   35 suites   4m 33s ⏱️
107 tests  81 ✅ 26 💤 0 ❌
795 runs  769 ✅ 26 💤 0 ❌

Results for commit ac3fc67.

♻️ This comment has been updated with latest results.

[github-actions]

Unit Test Performance Difference

Test Suite	$Status$	Time on main	$±Time$	$±Tests$	$±Skipped$	$±Failures$	$±Errors$
LongitudinalGSF	💚	$59.44$	$-58.27$	$+2$	$0$	$0$	$0$
LongitudinalQuantiles	💚	$42.29$	$-1.29$	$0$	$0$	$0$	$0$
LongitudinalSteinFojo	👶		$+1.11$	$+10$	$+2$	$0$	$0$
stan_functions	💚	$47.58$	$-1.47$	$0$	$0$	$0$	$0$

Additional test case details

Test Suite	$Status$	Time on main	$±Time$	Test Case
LongitudinalGSF	💚	$29.31$	$-29.30$	Can_recover_known_distributional_parameters_from_a_full_GSF_joint_model
LongitudinalGSF	💚	$29.51$	$-29.02$	Non_Centralised_parameterisation_compiles_without_issues
LongitudinalQuantiles	💚	$36.59$	$-1.11$	Test_that_LongitudinalQuantities_works_as_expected
LongitudinalSteinFojo	👶		$+0.01$	Can_recover_known_distributional_parameters_from_a_SF_joint_model
LongitudinalSteinFojo	👶		$+0.42$	Centralised_parameterisation_compiles_without_issues
LongitudinalSteinFojo	👶		$+0.11$	LongitudinalSteinFojo_works_as_expected_with_default_arguments
LongitudinalSteinFojo	👶		$+0.46$	Non_Centralised_parameterisation_compiles_without_issues
LongitudinalSteinFojo	👶		$+0.11$	Print_method_for_LongitudinalSteinFojo_works_as_expected

Results for commit fbb1422

♻️ This comment has been updated with latest results.

[github-actions]

Code Coverage Summary

Filename                       Stmts    Miss  Cover    Missing
---------------------------  -------  ------  -------  --------------------------------
R/brier_score.R                  166       0  100.00%
R/DataJoint.R                     76       2  97.37%   264, 270
R/DataLongitudinal.R             119       1  99.16%   245
R/DataSubject.R                   69       1  98.55%   124
R/DataSurvival.R                  74       1  98.65%   146
R/defaults.R                      10       6  40.00%   18-57, 84
R/generics.R                      19       1  94.74%   49
R/JointModel.R                   122       8  93.44%   142-144, 194, 198, 240, 286, 292
R/JointModelSamples.R             54       0  100.00%
R/Link.R                          55       4  92.73%   159-162
R/LinkComponent.R                 47       5  89.36%   100, 118, 132-149
R/LongitudinalGSF.R               64       0  100.00%
R/LongitudinalModel.R             35      12  65.71%   68-83
R/LongitudinalQuantities.R        85       8  90.59%   100-107
R/LongitudinalRandomSlope.R       27       0  100.00%
R/LongitudinalSteinFojo.R         57       8  85.96%   113-135
R/Parameter.R                     14       0  100.00%
R/ParameterList.R                 42       1  97.62%   184
R/Prior.R                        236       8  96.61%   480, 576, 588-606
R/Quantities.R                   105       0  100.00%
R/settings.R                      11      11  0.00%    40-53
R/simulations_gsf.R               41       0  100.00%
R/simulations_os.R                11       5  54.55%   35-39
R/simulations_rs.R                21       0  100.00%
R/simulations_sf.R                38      38  0.00%    17-107
R/simulations.R                  101       1  99.01%   128
R/StanModel.R                     15       0  100.00%
R/StanModule.R                   177       6  96.61%   199-200, 242, 253, 388, 416
R/SurvivalExponential.R           10       0  100.00%
R/SurvivalLoglogistic.R           11       0  100.00%
R/SurvivalModel.R                 19       0  100.00%
R/SurvivalQuantities.R           151       6  96.03%   173-178
R/SurvivalWeibullPH.R             11       0  100.00%
R/utilities.R                    145       1  99.31%   13
R/zzz.R                            2       2  0.00%    3-12
TOTAL                           2240     136  93.93%

Diff against main

Filename                     Stmts    Miss  Cover
-------------------------  -------  ------  --------
R/LongitudinalSteinFojo.R      +57      +8  +85.96%
R/simulations_sf.R             +38     +38  +100.00%
R/simulations.R                 +2       0  +0.02%
TOTAL                          +97     +46  -1.87%

Results for commit: ac3fc67

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[danielinteractive]

Ok for now then. I guess as a future idea one could in general enable "constant" priors as it seems brms also has which is then dynamically generating corresponding transformed parameters code. This could be useful in general and as a side product make GSF with constant prior on the mixture weight give the SF model.