Implements shape-based clustering algorithms for multidimensional longitudinal data based on the Fréchet distance. It implements two main methods: MFKmL (Multidimensional Fréchet distance-based K-means for Longitudinal data), an extension of the K-means algorithm using the Fréchet distance originally developed in the 'kmlShape' package, adapted for multidimensional trajectories; and SFKmL (Sparse multidimensional Fréchet distance-based K-medoids for Longitudinal data), a K-medoids-based clustering algorithm that incorporates variable selection. These tools are designed to enhance clustering performance in high-dimensional longitudinal data settings, particularly those with time delays, variations in trajectory speed, irregular sampling intervals, and noise. This package implements methods derived from Kang et al. (2023) <doi:10.1007/s11222-023-10237-z>.

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.

In order to achieve accurate estimation without sparsity assumption on the precision matrix, element-wise inference on the precision matrix, and joint estimation of multiple Gaussian graphical models, a novel method is proposed and efficient algorithm is implemented.
    FLAG() is the main function given a data matrix, and FlagOneEdge() will be used when one pair of random variables are interested where their indices should be given.
    Flexible and Accurate Methods for Estimation and Inference of Gaussian Graphical Models with Applications, see Qian Y (2023) <doi:10.14711/thesis-991013223054603412>, Qian Y, Hu X, Yang C (2023) <doi:10.48550/arXiv.2306.17584>.