Qolmat

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* CICD now relies on Node.js 20
* New tests for comparator.py and data.py

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* ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple
* File preprocessing.py added with classes new MixteHGBM, BinTransformer, OneHotEncoderProjector and WrapperTransformer providing tools to manage mixed types data
* Tutorial plot_tuto_categorical showcasing mixed type imputation
* Titanic dataset added
* accuracy metric implemented
* metrics.py rationalized, and split with algebra.py

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* RPCA algorithms now start with a normalizing scaler
* The EM algorithms now include a gradient projection step to be more robust to colinearity
* The EM algorithm based on the Gaussian model is now initialized using a robust estimation of the covariance matrix
* A bug in the EM algorithm has been patched: the normalizing matrix gamma was creating a sampling biais
* Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method
* The ImputeRegressor class now handles the nans by `row` by default
* The metric `frechet` was not correctly called and has been patched
* The EM algorithm with VAR(p) now fills initial holes in order to avoid exponential explosions

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* RPCA Noisy now has separate fit and transform methods, allowing to impute efficiently new data without retraining
* The class ImputerRPCA has been splitted between a class ImputerRpcaNoisy, which can fit then transform, and a class ImputerRpcaPcp which can only fit_transform
* The class SoftImpute has been recoded to better fit the architecture, and is more tested
* The class RPCANoisy now relies on sparse matrices for H, speeding it up for large instances

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* Hotfix reference to tensorflow in the documentation, when it should be pytorch
* Metrics KL forest has been removed from package
* EM imputer made more robust to colinearity, and transform bug patched
* CICD made faster with mamba and a quick test setting

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* 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