Version 0.1.0

• 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