Provides a compact variation of the usual syntax of function
  declaration, in order to support tidyverse-style quasiquotation of a
  function's arguments and body.

The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).

Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.

Implementation of models for the controlled introduction of errors in 
	classification datasets. This package contains the noise models described in 
	Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, 
	attributes and both simultaneously.

Cross-Entropy optimisation of unconstrained deterministic and noisy
    functions illustrated in Rubinstein and Kroese (2004, ISBN:
    978-1-4419-1940-3) through a highly flexible and customisable function which 
    allows user to define custom variable domains, sampling distributions,
    updating and smoothing rules, and stopping criteria. Several built-in
    methods and settings make the package very easy-to-use under standard
    optimisation problems.