docs: Added resampled M-test and MLL test to the concepts section. Le…

…ft Serafini et al., 2024 as in-prep, until pre-print is submitted.

docs/concepts/evaluations.rst

@@ -30,7 +30,7 @@ consistency tests and they verify whether a forecast in consistent with an obser
 that can be used to compare the performance of two (or more) competing forecasts.
 PyCSEP implements the following evaluation routines for grid-based forecasts. These functions are intended to work with
 :class:`GriddedForecasts<csep.core.forecasts.GriddedForecast>` and :class:`CSEPCatalogs`<csep.core.catalogs.CSEPCatalog>`.
-Visit the :ref:`catalogs reference<catalogs-reference>` and the :ref:`forecasts reference<forecasts-reference>` to learn
+Visit the :ref:`catalogs reference<catalogs-reference>` and the :ref:`forecasts reference<forecast-reference>` to learn
 more about to import your forecasts and catalogs into PyCSEP.
 
 .. note::
@@ -105,6 +105,8 @@ Consistency tests
    magnitude_test
    pseudolikelihood_test
    calibration_test
+   resampled_magnitude_test
+   MLL_magnitude_test
 
 Publication reference
 =====================
@@ -114,13 +116,16 @@ Publication reference
 3. Magnitude test (:ref:`Savran et al., 2020<savran-2020>`)
 4. Pseudolikelihood test (:ref:`Savran et al., 2020<savran-2020>`)
 5. Calibration test (:ref:`Savran et al., 2020<savran-2020>`)
+6. Resampled Magnitude Test (Serafini et al., in-prep)
+7. MLL Magnitude Test (Serafini et al., in-prep)
 
 ****************************
 Preparing evaluation catalog
 ****************************
 
 The evaluations in PyCSEP do not implicitly filter the observed catalogs or modify the forecast data when called. For most
 cases, the observation catalog should be filtered according to:
+
     1. Magnitude range of the forecast
     2. Spatial region of the forecast
     3. Start and end-time of the forecast

docs/getting_started/theory.rst

@@ -1019,7 +1019,7 @@ on the figure by default.
 Resampled Magnitude Test
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Aim: Perform the resampled magnitude test for catalog-based forecasts (Serafini et al., 2024),
+Aim: Perform the resampled magnitude test for catalog-based forecasts (Serafini et al., , in-prep),
 which is a correction to the original M-test implementation that is biased to the total N of
 events.
 
@@ -1064,7 +1064,7 @@ horizontal line. The quantile score for this forecast is
 Modified Multinomial Log-Likelihood (MLL) Magnitude Test
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-* Aim: Implements the modified Multinomial log-likelihood (MLL) magnitude test (Serafini et al., 2024).
+* Aim: Implements the modified Multinomial log-likelihood (MLL) magnitude test (Serafini et al., in-prep).
 
 * Method:  Calculates the test statistic distribution as: