Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>),
             Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>),
             SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
             Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>),
             Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>)
             and several simulation functions. It also allows dealing with intermittent demand based on the
             iETS framework (Svetunkov & Boylan, 2019, <doi:10.13140/RG.2.2.35897.06242>).

Implements several methods to estimate effects of generalized 
    time-varying treatment strategies on the mean of an outcome at one or more 
    selected follow-up times of interest. Specifically, the package implements 
    the time-smoothed inverse probability weighted estimators described in  
    McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, 
    non-monotonically, informatively, and sparsely measured in the data source. 
    The package also supports settings where outcomes are truncated by death, 
    i.e. some individuals die during follow-up which renders the outcome of 
    interest undefined at the follow-up time of interest.

We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.