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Programs

Coordinate transformation:
  • addcoord add coordinates to a GSLIB grid file
  • rotcoord 2-D coordinate rotation
Probability distribution weighting, transformation, and smoothing:
  • declus cell declustering
  • nscore normal score transformation
  • backtr back transformation from normal scores
  • trans general distribution transformation
  • histsmth smooth histogram / univariate distribution
  • scatsmth smooth scaterplot / bivariate distribution (see also bivplt)
Variograms:
  • gam variogram calculation of regular grid (use vargplt to plot results)
  • gamv variogram calculation of scattered data (use vargplt to plot results)
  • varmap variogram map / volume calculation (use pixelplt to plot results)
  • vmodel creates a variogram from an analytical model that can be plotted with vargplt
  • bigaus can be used to get the indicator variograms from a Gaussian or normal scores variogram
Kriging:
  • kb2d straightforward 2-D kriging
  • kt3d flexible 3-D kriging
  • cokb3d cokriging
  • ik3d indicator kriging (use postik to postprocess results)
Stochastic simulation:
  • draw simple Monte Carlo stochastic simulation
  • lusim LU matrix Gaussian simulation
  • sgsim sequential Gaussian simulation
  • gtsim truncated Gaussian simulation (uses the result of sgsim and proportion curves)
  • sisim sequential indicator simulation including categorical and continuous and Markov-Bayes (program bicalib is used to process calibration data)
  • pfsim probability field simulation
  • ellipsim 3-D ellipsoid simulation
  • anneal annealing-based post processing / simulation
  • sasim annealing-based simulation and cosimulation
  • postsim is used to post process a number of simulated realizations
PostScript plotting:
  • histplt histogram and cumulative histogram
  • probplt normal and lognnormal probability plot
  • scatplt scatterplot
  • qpplt Q-Q or P-P plot to compare two distributions
  • locmap gray and color 2-D data location map
  • pixelplt gray and color 2-D pixel map
  • bivplt plot a smoothed bivariate probability distribution with the marginal distributions