- 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 -- 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 -- 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 -- 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 -- 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 -- 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 -- Does histograms of multiple columns
of data. Example application: finding the distribution of a set of
similar readbacks that are sampled many times.
- 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 -- 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 -- 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 -- Computes running or blocked statistics of multiple columns. Example
applications: smoothing noisy data; finding running averages and error bars for time-series data.
- 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.