Author: Arnaud Le Rouzic (2007-2014),  Arne B. Gjuvsland (2010), Olivier Ariste (2010)

Title: Estimation of Intrinsic and Extrinsic Noise from Single-Cell
Author: Audrey Qiuyan Fu and Lior Pachter
Description: 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.

Description: 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>.

Title: Noise Models for Classification Datasets
Description: 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,
Imports: caret, nnet, e1071, FNN, classInt, ggplot2, ExtDist, lsr,
Maintainer: José A. Sáez <joseasaezm@ugr.es>
Packaged: 2022-10-14 13:05:44 UTC; joseasaezm

Title: Cross-Entropy Optimisation of Noisy Functions
Author: Flavio Santi [cre, aut] (<https://orcid.org/0000-0002-2014-1981>)
Description: 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