Sequential Normal Scores in Statistical Process Management

The methods discussed in this package are new non-parametric methods based on sequential normal scores 'SNS' (Conover et al (2017) ), designed for sequences of observations, usually time series data, which may occur singly or in batches, and may be univariate or multivariate. These methods are designed to detect changes in the process, which may occur as changes in location (mean or median), changes in scale (standard deviation, or variance), or other changes of interest in the distribution of the observations, over the time observed. They usually apply to large data sets, so computations need to be simple enough to be done in a reasonable time on a computer, and easily updated as each new observation (or batch of observations) becomes available. Some examples and more detail in 'SNS' is presented in the work by Conover et al (2019) .


Reference manual

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1.4.0 by Luis Benavides, 6 months ago

Browse source code at

Authors: Victor Tercero [aut] , Luis Benavides [aut, cre] , Jorge Merlo [ctb]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports parallel, stats, MASS

Suggests testthat, knitr, rmarkdown

See at CRAN