nixmass v1.3.1 (release date: 2025-05-09)
=========================================

Changes: 

* Included the `strict_mode` argument in the delta.snow models which does not perform 
strict date checking and allows for zero snow depth values at the beginnig of the time-series.


nixmass v1.3.0 (release date: 2025-05-08)
=========================================

Changes: 

* The HS2SWE model was added.


nixmass v1.2.5 (release date: 2024-12-18)
=========================================

Changes: 

* The plot function can now return a density plot on demand.
* Fixed a bug where the number of layers at the end of the winter season determined 
the returned layer matrices.


nixmass v1.2.4 (release date: 2024-12-14)
=========================================

Changes: 

* Fixed a bug which led to computational issues, i.e. warnigs about wrong index lengths.
* Added stop clause, when the input snow depth was probably not converted to meters.


nixmass v1.2.3 (release date: 2024-12-04)
=========================================

Changes: 

* The internal matrices of snow depth (h), swe and age are returned on demand.


nixmass v1.1.3 (release date: 2024-10-23)
=========================================
     
Changes: 

* The maximum bulk snow density increases smoothly with age following a trigonometric `atan` activation function.
* The swe.delta.snow function is backwards compatible with the original delta.snow formulation.


nixmass v1.0.3 (release date: 2024-06-11)
=========================================
     
Changes: 

* Date columns in data objects can now be either of class `character`, `Date` or `POSIXct`.
* Switched to Roxygen for package management.
* Stability in terms of failure tests has been strongly improved.


nixmass v1.0.2 (released ate: 2021-03-05)
=========================================
     
Changes: 

* Large preallocated matrices for keeping track of the main variables have been replaced by small matrices. They comprise of only three timesteps, i.e. timestep - 1, timestep, and timestep + 1. This makes computation much faster, and there is no more restriction of the data series length.
* The summary function has now a prettier output.

