Functions for estimating catalytic constant and Michaelis-Menten constant
            for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs 
            sampler based on the Bayesian framework.

Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems <doi:10.48550/arXiv.2203.01686>.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

Functions for computing critical values and implementing the one-sided/two-sided EL tests.

A novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

Provides functions to align curves and to compute mean curves based on the 
    elastic distance defined in the square-root-velocity framework. For more details on 
    this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>). 
    For more theoretical details on our methods and algorithms see 
    Steyer et al. (2023, <doi:10.1111/biom.13706>) and Steyer et al. (2023, <arXiv:2305.02075>).

Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.

Provides functions for the computation of functional elastic shape 
  means over sets of open planar curves. The package is particularly suitable for 
  settings where these curves are only sparsely and irregularly observed. It uses 
  a novel approach for elastic shape mean estimation, where planar curves are
  treated as complex functions and a full Procrustes mean is estimated from the
  corresponding smoothed Hermitian covariance surface. This is combined with the 
  methods for elastic mean estimation proposed in Steyer, Stöcker, Greven	(2022)
  <doi:10.1111/biom.13706>. See Stöcker et. al. (2022) <arXiv:2203.10522> for details.