Propagation of Uncertainty

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Documentation for package ‘propagate’ version 1.3-0

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bigcor Creating very large correlation/covariance matrices
colVarsC Fast column- and row-wise versions of variance coded in C++
conformity Conformity assessment with measurement uncertainty (JCGM 106:2012)
cor2cov Converting a correlation matrix into a covariance matrix
corDist Correlated Monte Carlo samples of input quantities with arbitrary distributions
datasets Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
doGauge Gauge propagate against published examples of measurement uncertainty evaluation
evalDerivs Utility functions for creating gradient and Hessian matrices with symbolic derivatives and evaluating them in an environment
fitDistr Fitting distributions to observations/Monte Carlo simulations
H.1 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
H.2 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
H.3 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
H.4 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
H.5 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
H.6 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
interval Range of a function over intervals of its input variables
kurtosis Skewness and (excess) Kurtosis of a vector of values or of the columns of a matrix/data frame
makeDat Create the input data for 'propagate' from the workspace, a file or an uncertainty budget
makeGrad Utility functions for creating gradient and Hessian matrices with symbolic derivatives and evaluating them in an environment
makeHess Utility functions for creating gradient and Hessian matrices with symbolic derivatives and evaluating them in an environment
mixCov Aggregating covariance matrices and/or variance vectors into a single covariance matrix
numGrad Functions for creating Gradient and Hessian matrices by numerical differentiation (Richardson's method) of the partial derivatives
numHess Functions for creating Gradient and Hessian matrices by numerical differentiation (Richardson's method) of the partial derivatives
plot.conformity Conformity assessment with measurement uncertainty (JCGM 106:2012)
plot.propagate Plotting function for 'propagate' objects
plot.propagateMV Uncertainty propagation for several output quantities (JCGM 102:2011)
predictNLS Confidence/prediction intervals for (weighted) nonlinear models based on uncertainty propagation
print.conformity Conformity assessment with measurement uncertainty (JCGM 106:2012)
print.interval Range of a function over intervals of its input variables
print.propagateMV Uncertainty propagation for several output quantities (JCGM 102:2011)
propagate Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation
propagateMV Uncertainty propagation for several output quantities (JCGM 102:2011)
QUAM.A4 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
QUAM.A5 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
rDistr Creating random samples from a variety of useful distributions
report PDF report of one or several uncertainty evaluations
rowVarsC Fast column- and row-wise versions of variance coded in C++
S1.9.3 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
S1.9.5 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
S2.9.4 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
S2.9.5 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
skewness Skewness and (excess) Kurtosis of a vector of values or of the columns of a matrix/data frame
sobol Sobol' sensitivity indices from a propagate object or sample matrices
statVec Transform an input vector into one with defined mean and standard deviation
stochContr Stochastic contribution analysis of the Monte Carlo output
summary.propagate Summary function for 'propagate' objects
summary.propagateMV Uncertainty propagation for several output quantities (JCGM 102:2011)
TN.E11 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
TN.E2 Datasets from the GUM (JCGM 100:2008), its Supplements 1 and 2 (JCGM 101:2008, JCGM 102:2011), NIST TN 1900 and EURACHEM QUAM
WelchSatter Effective degrees of freedom: Welch-Satterthwaite formula and its generalization to correlated components