Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.

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>.