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propagate (version 1.3-0)

Propagation of Uncertainty

Description

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation. Calculations of propagated uncertainties are based on matrix calculus including covariance structure according to Arras 1998 (first order), Wang & Iyer 2005 (second order) and the Monte Carlo method of GUM Supplement 1 (2008) .

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Install

install.packages('propagate')

Monthly Downloads

694

Version

1.3-0

License

GPL (>= 2)

Maintainer

Andrej-Nikolai Spiess

Last Published

November 10th, 2026

Functions in propagate (1.3-0)

Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM

Gauge propagate against published examples of measurement uncertainty evaluation

Conformity assessment with measurement uncertainty (JCGM 106:2012)

Range of a function over intervals of its input variables

Correlated Monte Carlo samples of input quantities with arbitrary distributions

Fitting distributions to observations/Monte Carlo simulations

Converting a correlation matrix into a covariance matrix

Creating very large correlation/covariance matrices

Create the input data for 'propagate' from the workspace, a file or an uncertainty budget

Effective degrees of freedom: Welch-Satterthwaite formula and its generalization to correlated components

Utility functions for creating gradient and Hessian matrices with symbolic derivatives and evaluating them in an environment

Plotting function for 'propagate' objects

Skewness and (excess) Kurtosis of a vector of values or of the columns of a matrix/data frame

Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation

Creating random samples from a variety of useful distributions

Aggregating covariance matrices and/or variance vectors into a single covariance matrix

Functions for creating Gradient and Hessian matrices by numerical differentiation (Richardson's method) of the partial derivatives

Confidence/prediction intervals for (weighted) nonlinear models based on uncertainty propagation

Uncertainty propagation for several output quantities (JCGM 102:2011)

Fast column- and row-wise versions of variance coded in C++

Stochastic contribution analysis of the Monte Carlo output

Sobol' sensitivity indices from a propagate object or sample matrices

summary.propagate

Summary function for 'propagate' objects

Transform an input vector into one with defined mean and standard deviation

PDF report of one or several uncertainty evaluations