Heteroskedastic Gaussian Process Modeling and Design under Replication

Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) . The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.


hetGP 1.1.0

change / hetGP 1.0.2

  • better handling of the penalty term
  • update the documentation, rename nu2_hat by nu_hat for consistency with JCGS paper
  • several performance improvements (e.g., predict with xprime argument)
  • contour finding infill criteria related to arXiv:1807.06712
  • add Expected Improvement criterion (beta version)
  • correct bug with known mean not used for initialization with mleHetGP/TP
  • add leave one out predictions

hetGP 1.0.2

change / hetGP 1.0.1

  • homTP/heTP update methods
  • option settings$return.hom to return or not modHom models with hetGP and hetTP
  • new functions to export/import hetGP objects, including a robust re-computation of inverse covariance matrices
  • accordingly, predict now only threshold negative predictive variances occuring due to numerical instability with Cholesky inverse
  • better initialization with mleHetTP
  • improved warnings and error handling

hetGP 1.0.1

change / hetGP 1.0.0

  • add Student-t modeling (equivalent of mleHetGP and mleHomGP, with predict functions)
  • various typos and bugs corrections
  • now works on Solaris

Reference manual

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1.1.1 by Mickael Binois, a month ago

Browse source code at https://github.com/cran/hetGP

Authors: Mickael Binois , Robert B. Gramacy

Documentation:   PDF Manual  

LGPL license

Imports Rcpp, MASS, methods, DiceDesign

Suggests knitr, monomvn, lhs

Linking to Rcpp

See at CRAN