Gaussianize

Transforms each column of data matrix X to normality using the inverse Rosenblatt transform. Tolerant to missing values (NA entries).

gaussianize(X, jitter = FALSE)

Arguments

X

data matrix (2D array)

jitter

boolean variable ; if TRUE, add jitter to data to prevent ties

Value

gaussianized data matrix, Xn

References

Emile-Geay, J., and M. Tingley (2016), Inferring climate variability from nonlinear proxies: application to palaeo-enso studies, Climate of the Past, 12 (1), 31–50, doi:10.5194/cp-12-31-2016.

Van Albada, S.J., Robinson P.A. (2006), Transformation of arbitrary distributions to the normal distribution with application to EEG test-retest reliability. J Neurosci Meth, doi:10.1016/j.jneumeth.2006.11.004

See also

Other utility: askUser(), concatEnsembleTimeseries(), convertAD2BP(), convertBP2AD(), createChronMeasInputDf(), generateEnsembleFromUncertainty(), getLastVarString(), getOs(), heuristicUnits(), loadRemote(), pullInstance(), simulateAutoCorrelatedUncertainty(), stringifyVariables(), surrogateDataFun()

Author

Julien Emile-Geay

Nick McKay