changelog.md

In italo-goncalves/geomod3D: Implicit 3D Geological Modeling and Geostatistics

Version 0.0.0.9003

• GP training now uses a dedicated genetic algorithm
• Bug fixes for Cauchy covariance
• Covariance matrix calculations implemented as proper internal methods (no more covfun slots)
• Removed weights argument in GP and SPGP
• Added data as default value for pseudo_inputs in SPGP
• Constant and linear covariances now supported
• Testing a more stable method for matrix inversions and Cholesky decomposition

Version 0.0.0.9002

• Corrected a bug in SPGP object initialization the prevented the use of points3DDataFrame objects as pseudo-inputs
• Corrected a bug that caused the GetContacts() method to convert the labels to factors
• Corrected a bug in the Predict() method that caused an error for SPGP_geomod objects
• Added a default for the midrange parameter in anisotropy3d() method
• Corrected a bug in the Simulate() method for the standard GP
• Added regularization to the smoothing covariance matrix for SPGP simulations
• Corrected a bug in the Simulate() method for the SPGP, where the mean was not added back when making smoothed simulations
• Corrected a bug in the Simulate() method for the SPGP, where the mean was added improperly during the simulation, generating inflated values
• SPGP simulation now uses only the approximated variance as prior to avoid value inflation
• Creation of the covarianceModel3D class to handle the covariances and the nugget effect on the same object
• points3DDataFrame can now be initialized with no arguments
• Corrected the computations of the log-likelihood
• GP_geomod can now handle missing data labels
• SPGP method Predict now outputs a value that measures the quality of the sparse approximation
• GP_geomod now uses the same covariance model for all classes
• Improved documentation

Version 0.0.0.9001

• Formalization of structural data as a directions3DDataFrame object
• Support for block models as blocks3DDataFrame objects
• Inclusion of Sparse Gaussian Processes for regression and classification (implicit modeling)
• Support for simulations
• Removed support for covariance matrices in variogram form
• Support of cross-validation for Sparse Gaussian Processes
• Implementation of as.data.frame() method
• Unification of covariance methods for all kinds of spatial objects
• Added a parameter for nugget effect of structural data in the GP object
• Calculation of the implicit model's probabilities is now done through sampling

italo-goncalves/geomod3D documentation built on May 24, 2019, 2:49 p.m.