Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.

Starting from a given object representing a fitted model (within
  a certain set of model classes) whose linear predictor includes some ordered 
  factor(s) among the explanatory variables, a new model is constructed and
  fitted where each named factor is replaced by a single numeric score, suitably
  chosen so that the new variable produces a fit comparable with the standard
  methodology based on a set of polynomial contrasts. Reference: Azzalini (2023) 
  <doi:10.1002/sta4.624>.

Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. 
    Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. 
    In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.

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>).