This is a fast and flexible implementation of the Kalman
        filter and smoother, which can deal with NAs. It is entirely written in C and relies fully on linear algebra subroutines contained in
        BLAS and LAPACK. Due to the speed of the filter, the fitting of
        high-dimensional linear state space models to large datasets
        becomes possible. This package also contains a plot function
        for the visualization of the state vector and graphical
        diagnostics of the residuals.

Fast and flexible Kalman filtering and smoothing implementation utilizing sequential processing, designed for efficient parameter estimation through maximum likelihood estimation. Sequential processing is a univariate treatment of a multivariate series of observations and can benefit from computational efficiency over traditional Kalman filtering when independence is assumed in the variance of the disturbances of the measurement equation. Sequential processing is described in the textbook of Durbin and Koopman (2001, ISBN:978-0-19-964117-8). 'FKF.SP' was built upon the existing 'FKF' package and is, in general, a faster Kalman filter/smoother.

Implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations.
  Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods
  for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected
  covariates.
  The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.

Tools and features for "Exploratory Landscape Analysis (ELA)" of
	single-objective continuous optimization problems.
    Those features are able to quantify rather complex properties, such as the
    global structure, separability, etc., of the optimization problems.