Title: Two-dimensional peak sentinel tool for GC x GC-HRTOFMS
Description: This function assign and integrate target peaks in
Packaged: 2012-08-11 05:22:20 UTC; calculate

Title: Total Survey Error (Independent Samples)
Description: Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
Note: 'TSEind' is a companion package to 'TSE'. Both calculate TSE for
your surveys, but use 'TSEind' if your surveys and the "gold
standard" survey have independent samples, and use 'TSE' if
your surveys and the "gold standard" survey have paired

Authors@R: person("Jiri", "Tomcala", email = "jiri.tomcala@vsb.cz", role = c("aut", "cre"))
Description: Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.