library->package

Author: dilpath

doc/analysis.rst

@@ -2,7 +2,7 @@ Analysis
 ========
 
 After using PEtab Select to perform model selection, you may want to operate on all "good" calibrated models.
-The PEtab Select Python library provides some methods to help with this. Please request any missing methods.
+The PEtab Select Python package provides some methods to help with this. Please request any missing methods.
 
 See the Python API docs for the :class:`petab_select.Models` class, which provides some methods. In particular, :attr:`petab_select.Models.df` can be used
 to get a quick overview over all models, as a pandas dataframe.
@@ -13,7 +13,7 @@ or compute "weights" (e.g. Akaike weights).
 Model hashes
 ^^^^^^^^^^^^
 
-Model hashes are special objects in the library, that are generated from model-specific information that is unique within a single PEtab Select problem.
+Model hashes are special objects in the package, that are generated from model-specific information that is unique within a single PEtab Select problem.
 
 This means you can reconstruct the model given some model hash. For example, with this model hash `M1-000`, you can reconstruct the :class:`petab_select.ModelHash` from a string, then reconstruct the :class:`petab_select.Model`.
 

doc/index.rst

@@ -9,7 +9,7 @@ Welcome to PEtab Select's documentation!
 PEtab Select brings
 `model selection <https://en.wikipedia.org/wiki/Model_selection>`_ to
 `PEtab <https://petab.readthedocs.io/>`_. PEtab Select comprises file
-formats, a Python library and a command line interface.
+formats, a Python package and a command line interface.
 
 Model selection is the process of choosing the best model from a set of
 candidate models. PEtab Select provides a standardized and compact way to
@@ -40,7 +40,7 @@ PEtab Select is well-integrated with:
   (`example <https://pypesto.readthedocs.io/en/latest/example/model_selection.html>`__)
 
 Other model calibration tools can easily be integrated using the provided
-Python library or command line interface.
+Python package or command line interface.
 
 Installation
 ------------

doc/problem_definition.rst

@@ -9,7 +9,7 @@ Model selection problems for PEtab Select are defined by the following files:
 
 The different file formats are described below. The YAML file formats
 come with a YAML-formatted JSON schema, such that these files can be
-easily worked with independently of the PEtab Select library.
+easily worked with independently of the PEtab Select package.
 
 1. Selection problem
 --------------------
@@ -155,7 +155,7 @@ Brief format description
 - ``model_subspace_id``: Same as in the model space files.
 - ``model_subspace_indices``: The indices that locate this model in its model subspace.
 - ``criteria``: The value of the criterion by which model selection was performed, at least. Optionally, other criterion values too.
-- ``model_hash``: The model hash, generated by the PEtab Select library. The format is ``[MODEL_SUBSPACE_ID]-[MODEL_SUBSPACE_INDICES_HASH]``. If all parameters are in the model are defined like ``0;estimate``, then the hash is a string of ``1`` and ``0``, for parameters that are estimated or not, respectively.
+- ``model_hash``: The model hash, generated by the PEtab Select package. The format is ``[MODEL_SUBSPACE_ID]-[MODEL_SUBSPACE_INDICES_HASH]``. If all parameters are in the model are defined like ``0;estimate``, then the hash is a string of ``1`` and ``0``, for parameters that are estimated or not, respectively.
 - ``model_subspace_petab_yaml``: Same as in model space files.
 - ``estimated_parameters``: Parameter estimates, including all estimated parameters that are not in the model selection problem; i.e., parameters that are set to be estimated in the model subspace PEtab problem but don't appear in the column header of the model space file.
 - ``iteration``: The iteration of model selection in which this model appeared.