Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.

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>).

Fits Bayesian hierarchical piecewise regression models with 
    multiple logistic-smoothed change-points. Non-linear parameters (change-point 
    locations and transition sharpness) and linear parameters can each be 
    conditioned on covariates and factors via flexible design matrices. 
    A random-intercept structure is supported for any parameter. Spike-and-slab 
    regularization is supported for selecting the number of breakpoints. 
    Posterior inference uses a Metropolis-within-Gibbs sampler implemented 
    in 'Rust' for speed. Methods are based on the smooth transition 
    piecewise regression model of Bacon and Watts (1971) <doi:10.2307/2334389> 
    and variable selection spike-and-slab priors of Kuo and Mallick (1998) 
    <https://www.jstor.org/stable/25053023>.

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.