Voice (303) 497-1711, FAX (303) 497-2483 Cell (303) 725-3199 Email nychka "at" ucar "dot" edu BIO (text) CV Everything: (html) (pdf) (LaTeX) NSF style (but topical): (pdf) (LaTeX) One pager: (pdf) (LaTeX)
Material for Workshop on Environmental Analytics, CU - Boulder, June, 2014
Material for APPM course, CU - Boulder, June, 2014
Joint NCAR U Wyoming short course, Boulder, June, 2013
CBMS Lecture Series, University of Washington, Seattle, August 2012
Institute of Mathematical Sciences, National University of Singapore, Feb 28 - March 4 2011
Useful datasets and source for interpreting the NARCCAP model output..
An example of a nonstationary spatial process and spatial extremes .
Regional climate model and observed precipitation data for the Colorado Front Range.
LatticeKrig is a spatial method for large data sets that builds on compactly supported basis functions, Markov random fields and sparse matrix methods.
¥ Beta version LatticeKrig_3.3.tar.gz July-11-2014. Depends on fields and spam packages.
fields (home page) is a collection of programs based in R for curve and function fitting with an emphasis on spatial data and flexible covariance functions for Kriging.
¥ Beta version -- fields_7.1.2.tar.gz Septemebr -15-2014.
¥ Binaries and source supplied as a Software package by the Comprehensive R Archive Network (CRAN) (This may be an older but perhaps more stable version.)
Short course CD A directory with the lectures, source code, R binaries and R packages used in the ENAR short course, MAR 2009.
spam is a collection of functions based in R/Fortran for sparse matrix algebra. Written by Reinhard Furrer with the attention to detail that have made the Swiss famous! The fields pacakage uses these functions for spatial analysis of large datasets. Current CRAN version: Version 0.23 SEP-2010.
Major fields functions: Tps: Thin Plate spline regression Krig: Spatial process estimate (Kriging) This function allows you to supply a covariance function as R code, uses sparse matrix methods from the spam package and can handle large data sets. mKrig (micro Krig ) and fastTps Fast spatial prediction that can take advantage of compactly supported covariance functions and handle big data sets cover.design: Finds a space filling design as.image, image.plot, quilt.plot, crop.image, average.image, designer.colors: Some useful functions for working with image data on 2-d grids and color scales sreg, qsreg : 1-d smoothing splines and 1-d quantile splines
There are also generic functions that support these methods such as: plot diagnostic plots of fit summary statistical summary of fit surface graphical display of fitted surface predict, predict.se evaluation fit and prediction error at arbitrary points
Neural Networks Package. Estimates a function using a single hidden layer neural network by nonlinear least squares. The fitting algorithm is both robust and accurate. Has supporting functions for diagnostics, GCV and graphing.
Doug Nychka (contact), Stephen Ellner and Barbara Bailey nnreg_1.1.tar.gz (59K)
Lyapunov Exponents fit by Neural Networks
Fits nonlinear autoregressive maps to multivariate time series data and estimates global and local Lyapunov Exponents. Doug Nychka (contact), Stephen Ellner and Barbara Bailey lenns_1.0.tar.gz (25K)