Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).

Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References:  Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.

Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.

In order to smoothly animate the transformation of polygons and 
    paths, many aspects needs to be taken into account, such as differing number 
    of control points, changing center of rotation, etc. The 'transformr' 
    package provides an extensive framework for manipulating the shapes of 
    polygons and paths and can be seen as the spatial brother to the 'tweenr' 
    package.

Provides geomorphology-based hydrological modelling for transferring
    streamflow measurements from gauged to ungauged catchments. Inverse
    modelling makes it possible to estimate net rainfall from streamflow
    measurements following Boudhraâ et al. (2018)
    <doi:10.1080/02626667.2018.1425801>. The resulting net rainfall is then
    interpolated to ungauged catchments to simulate streamflow
    following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.