mc.left

<p>Calculates left medcouple (MLC).
The function is called by <code><a rd-options="" href="/link/KRDetect.outliers.changepoint?package=envoutliers&version=1.1.0" data-mini-rdoc="envoutliers::KRDetect.outliers.changepoint">KRDetect.outliers.changepoint</a></code> and is not intended for use by regular users of the package.</p>

Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).

Martina Campulova

envoutliers

Methods for Identification of Outliers in Environmental Data

Roman Campula

mc.left function

<p>Calculates left medcouple (MLC).
The function is called by <code><a rd-options='' href='KRDetect.outliers.changepoint'>KRDetect.outliers.changepoint</a></code> and is not intended for use by regular users of the package.</p>

mc.left: Left medcouple (LMC) - Only intended for developer use

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