Updates for PyPi

.pypirc

+[distutils]
+index-servers =
+    pypi
+
+[pypi]
+repository = https://pypi.org/project/bayesvalidrox/
+username = __token__
+password = pypi-bf547817-6852-4f59-90e2-3f3e3ee10b91

pyproject.toml

-[project]
-name = "bayesvalidrox"
-version = "1.1.0"
-require-python = ">=3.9"
-classifiers = [
-    "Programming Language :: Python :: 3",
-    "License :: OSI Approved :: MIT License",
-    "Operating System :: OS Independent",
-]
-
 [build-system]
 requires = [
    "setuptools >= 40.6.0",
@@ -19,7 +9,3 @@ build-backend = "setuptools.build_meta"
 pythonpath = [
   ".", "src",
 ]
-
-[project.urls]
-Homepage = "https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox"
-Source = "https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox"
\ No newline at end of file

setup.cfg

 [metadata]
 name = bayesvalidrox
-version = 1.0.0
+version = 1.1.0
 author = Farid Mohammadi, Rebecca Kohlhaas
 author_email = farid.mohammadi@iws.uni-stuttgart.de, rebecca.kohlhaas@iws.uni-stuttgart.de
 description = An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.

src/bayesvalidrox.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: bayesvalidrox
-Version: 1.0.0
+Version: 1.1.0
 Summary: An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
 Home-page: https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation
 Author: Farid Mohammadi, Rebecca Kohlhaas
@@ -31,14 +31,33 @@ Requires-Dist: umbridge==1.2.4
 # BayesValidRox
 
 <div align="center">
-  <img src="https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation/-/raw/master/docs/logo/bayesvalidrox-logo.png" alt="bayesvalidrox logo"/>
+  <img src="https://git.iws.uni-stuttgart.de/inversemodeling/bayesian-validation/-/raw/master/docs/logo/BVRLogoV03_longtext.png" alt="bayesvalidrox logo"/>
 </div>
 
 An open-source, object-oriented Python package for surrogate-assisted Bayesain Validation of computational models.
 This framework provides an automated workflow for surrogate-based sensitivity analysis, Bayesian calibration, and validation of computational models with a modular structure.
 
+[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+
+## Features
+* Surrogate modeling with Polynomial Chaos Expansion
+* Global sensitivity analysis using Sobol Indices
+* Bayesian calibration with MCMC using `emcee` package
+* Bayesian validation with model weights for multi-model setting
+
+## Resources
+The following resources are useful to get started on working with BayesValidRox:
+* [BayesValidRox website](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/)
+* [User guide](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/packagedescription.html)
+* [Tutorial](https://pages.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/tutorial.html)
+
+Important links:
+* [GitLab](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox)
+* [Changelog](https://git.iws.uni-stuttgart.de/inversemodeling/bayesvalidrox/-/blob/master/CHANGELOG.md?ref_type=heads)
+
 ## Authors
 - [@farid](https://git.iws.uni-stuttgart.de/farid)
+- [@RKohlhaas](https://git.iws.uni-stuttgart.de/RKohlhaas)
 
 ## Installation
 The best practive is to create a virtual environment and install the package inside it.
@@ -62,25 +81,24 @@ and installing the version on the master branch can be done by cloning this repo
   pip install .
 ```
 
-## Features
-* Surrogate modeling with Polynomial Chaos Expansion
-* Global sensitivity analysis using Sobol Indices
-* Bayesian calibration with MCMC using `emcee` package
-* Bayesian validation with model weights for multi-model setting
-
 ## Requirements
-* numpy==1.22.1
-* pandas==1.2.4
-* joblib==1.0.1
-* matplotlib==3.4.2
+python 3.10:
+* numpy>=1.23.5
+* pandas==1.4.4
+* joblib==1.1.1
+* matplotlib==3.8.0
 * seaborn==0.11.1
-* scikit-learn==0.24.2
-* tqdm==4.61.1
+* scipy>=1.11.1
+* scikit-learn==1.3.1
+* tqdm>=4.61.1
 * chaospy==4.3.3
 * emcee==3.0.2
 * corner==2.2.1
-* h5py==3.2.1
-* statsmodels==0.13.2
+* h5py==3.9.0
+* statsmodels==0.14.2
+* multiprocess==0.70.16
+* datasets==2.20.0
+* umbridge==1.2.4
 
 ## TexLive for Plotting with matplotlib
 Here you need super user rights