Qolmat

-------------------

* VAR(p) EM sampler implemented, founding on a VAR(p) modelization such as the one described in `Lütkepohl (2005) New Introduction to Multiple Time Series Analysis`
* EM and RPCA matrices transposed in the low-level impelmentation, however the API remains unchanged
* Sparse matrices introduced in the RPCA implementation so as to speed up the execution
* Implementation of SoftImpute, which provides a fast but less robust alterantive to RPCA
* Implementation of TabDDPM and TsDDPM, which are diffusion-based models for tabular data and time-series data, based on Denoising Diffusion Probabilistic Models. Their implementations follow the work of Tashiro et al., (2021) and Kotelnikov et al., (2023).
* ImputerDiffusion is an imputer-wrapper of these two models TabDDPM and TsDDPM.
* Docstrings and tests improved for the EM sampler
* Fix ImputerPytorch
* Update Benchmark Deep Learning

-------------------

* Hyperparameters are now optimized in hyperparameters.py, with the maintained module hyperopt
* The Imputer classes do not possess a dictionary attribute anymore, and all list attributes have
been changed into tuple attributes so that all are not immutable
* All the tests from scikit-learn's check_estimator now pass for the class Imputer
* Fix MLP imputer, created a builder for MLP imputer
* Switch tensorflow by pytorch. Change Test, environment, benchmark and imputers for pytorch
* Add new datasets
* Added dcor metrics with a pattern-wise computation on data with missing values

-------------------

* Documentation improved, with the API information
* Bug patched, in particular for some logo display and RPCA imputation
* The PRSA online dataset has been modified, the benchmark now loads the new version with a single station
* More tests have been implemented
* Tests for compliance with the sklearn standards have been implemented (check_estimator). Some arguments are mutable, and the corresponding tests are for now ignored

-------------------

* Refacto cross validation
* Fix Readme
* Add test utils.plot

-------------------

* Improve test and RPCA

-------------------

* Use of pytest and mypy in github action, and tracking of the test cover
* Mise under licence BSD-1-Clause
* Improvement of the documentation
* Addition of a tensorflow extra along with the corresponding type of imputer
* New metrics for a better estimation of the error in terms of distribution
* Several imputers have been renamed
* Implementation of 75 tests, covering 57% of the code