- sddscorrelate
`sddscorrelate`()sddscorrelate -- Computes correlation coefficients and correlation significance between column data. Example application: finding correlations among time series data collected from process variables, and evaluating their signficance to find possible cause-and-effect relationships. - sddsdistest
`sddsdistest`()sddsdistest -- Performs statistical tests on data to determine whether the data is drawn from any of various distributions. Example application: determining if a component failure rate matches a Poisson distribution. - sddsenvelope
`sddsenvelope`()sddsenvelope -- Analyzes column data across pages to find minima, maxima, averages, standard-deviations, etc., on a row-by-row basis. Example application: finding the envelope and average of a set of waveforms. - sddseventhist
`sddseventhist`()sddseventhist -- Analyzes labeled events in a dataset to provide histograms of the occurences of each type of event. Can also histogram the overlap off all types of events with a single type of event. Example application: correlating the occurence times of alarm signals to determine which alarms usually occur together. - sddshist
`sddshist`()sddshist -- Does histograms of column data. Example application: finding the distribution of a readback that is sampled many times, or of particle coordinates from an accelerator tracking simulation. - sddshist2d
`sddshist2d`()sddshist2d -- Does two-dimensional histograms of column data. Example applications: finding the two-dimensional distribution of a pair of readbacks that are sampled many times, or of two particle coordinates (e.g., x and y position) from an accelerator tracking simulation. - sddsmultihist
`sddsmultihist`()sddsmultihist -- Does histograms of multiple columns of data. Example application: finding the distribution of a set of similar readbacks that are sampled many times. - sddsoutlier
`sddsoutlier`()sddsoutlier -- Eliminates statistical outliers from data. Example application: eliminating bad or nonrepresentative data points prior to searching for correlations with`sddscorrelate`

, or computing statistics with`sddsprocess`

. - sddsprocess
`sddsprocess`()sddsprocess -- Probably the most-used toolkit program, excepting`sddsplot`

. Allows creating new parameters and columns with user-specified equations; filtering and matching operations; printing, editing, scanning, and subprocess operations; statistical and waveform analysis of column data to produce new parameters; and much more. - sddsrowstats
`sddsrowstats`()sddsrowstats -- Computes row-by-row statistics across multiple columns of data, creating new columns to contain the statistics. Example application: finding the mean value of a set of readout values from time-series data collection, where each readout is in a separate column. - sddsrunstats
`sddsrunstats`()sddsrunstats -- Computes running or blocked statistics of multiple columns. Example applications: smoothing noisy data; finding running averages and error bars for time-series data. - sddsshiftcor
`sddsshiftcor`()sddsshiftcor -- Computes correlation coefficients between column data as a function of shift position of a reference column. Example application: finding correlations among time series data collected from process variables, including the possibility of time-lags between the process variables due to physical or data collection effects.