Estimation of Intrinsic and Extrinsic Noise from Single-Cell
Data
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.

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.  <http://arxiv.org/abs/1601.03334>.

Noise Models for Classification Datasets
person(given = "José A.",
family = "Sáez",
role = c("aut", "cre"),
email = "joseasaezm@ugr.es")
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.
caret, nnet, e1071, FNN, classInt, ggplot2, ExtDist, lsr,
stringr, RColorBrewer, RSNNS, C50

Cross-Entropy Optimisation of Noisy Functions
person(given = "Flavio",
family = "Santi",
role = c("cre", "aut"),
email = "flavio.santi@univr.it",
comment = c(ORCID = "0000-0002-2014-1981"))
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.