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Tools for Two-Dimensional Monte-Carlo Simulations

The package

mc2d provides a complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. It also includes various distributions frequently used in the risk assessment domain (pert, triangular, Bernoulli, empirical discrete and continuous, beta subjective, Minimum Quantile Information Distribution, ...).

Getting it

The stable version of mc2d can be installed from CRAN using:

install.packages("mc2d")
library("mc2d")

The development version of mc2d can be installed from GitHub (devtools needed):

if (!requireNamespace("devtools", quietly = TRUE))
   install.packages("devtools")
devtools::install_github("rpouillot/mc2d")
library("mc2d")

Check the NEWS here.

Documentation

See the manual and the vignettes distributed with the package.

Issues

Issues can be reported on https://github.com/rpouillot/mc2d/issues.

or directly to the maintainer Régis Pouillot: rpouillot@yahoo.fr

Citations

If you use mc2d, please cite:

R. Pouillot, M.-L. Delignette-Muller (2010), Evaluating variability and uncertainty in microbial quantitative risk assessment using two R packages. International Journal of Food Microbiology. 142(3):330-40

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Version

Install

install.packages('mc2d')

Monthly Downloads

4,909

Version

0.2.0

License

GPL (>= 2)

Maintainer

Last Published

July 17th, 2023

Functions in mc2d (0.2.0)

The Discrete Empirical Distribution

Draws a Tornado chart as provided by tornado (ggplot version).

Evaluates a Monte-Carlo model

Random Latin Hypercube Sampling

Apply Functions Over mc or mcnode Objects

Ratio of uncertainty and the variability

Draws a Tornado chart.

Evaluates a Two-Dimensional Monte Carlo Model in a Loop.

Creates a Stochastic mcnode Object using a Probability Tree

Plots Results of a Monte Carlo Simulation

Monte Carlo model

Histogram of a Monte Carlo Simulation

Computes Correlation between Inputs and Output in a mc Object (tornado) in the Variability Dimension;

Prints a mcnode or a mc Object

Tests mc and mcnode Objects

Maxima and Minima for mcnodes

Computes Correlation between Inputs and Output in a mc Object (tornado) in the Uncertainty Dimension

Build mcnode Objects from Data or other mcnode Objects

An Example of all Kind of mcnode

The Triangular Distribution

Spaghetti Plot of mc/mcnode Object

Summary of mcnode and mc Object

The Vectorized Multivariate Random Deviates

Creates Stochastic mcnode Objects

Monte Carlo Object

Sets or Gets the Default Number of Simulations.

Provides the Type of a mcnode Object

Output of Nodes

Quantiles of a mc Object

Unclasses the mc or the mcnode Object

Random Truncated Distributions

The (Modified) PERT Distribution

Graph of Running Statistics in the Variability or in the Uncertainty Dimension.

The Log Normal Distribution parameterized through its mean and standard deviation.

The BetaSubjective Distribution

Builds a Rank Correlation using the Iman and Conover Method.

The Bernoulli Distribution

The Generalised Beta Distribution

Dimension of mcnode and mc Objects

Finite, Infinite, NA and NaN Numbers in mcnode.

Histogram of a Monte Carlo Simulation (ggplot version)

Spaghetti Plot of `mc` or `mcnode` Object

MinimumQuantileInformation

Minimum Quantile Information Distribution

The Vectorized Multinomial Distribution

Utilities for multivariate nodes

An example on Escherichia coli in ground beef

The Dirichlet Distribution

Operations on mcnode Objects

The Continuous Empirical Distribution