## S3 method for class 'spatialDesign'
x[...]

Krig.df.to.lambda(df, D, guess = 1, tol = 1e-05) 
Krig.fdf (llam, info) 
Krig.fgcv (lam, obj) 
Krig.fgcv.model (lam, obj) 
Krig.fgcv.one (lam, obj) 
Krig.flplike (lambda, obj)
Krig.fs2hat (lam, obj) 
Krig.ftrace (lam, D) 
Krig.parameters (obj, mle.calc=obj$mle.calc)
Krig.updateY (out, Y, verbose = FALSE, yM=NA) 
Krig.which.lambda(out)
Krig.ynew (out, y=NULL, yM=NULL ) 
 



bisection.search (x1, x2, f, tol = 1e-07, niter = 25, f.extra = 
        NA, upcross.level = 0) 
 
cat.matrix (mat, digits = 8) 
 
cat.to.list (x, a) 
 
ceiling2 (m) 
 
describe (x) 

double.exp(x)

dyadic.2check( m,n,cut.p=2)
dyadic.check( n,cut.p=2) 

Exp.earth.cov (x1, x2, theta = 1) 
 
fast.1way  (lev, y, w = rep(1, length(y))) 
 
find.upcross (fun, fun.info, upcross.level = 0, guess = 1, tol = 
1e-05) 
 
gauss.cov (...) 


golden.section.search (ax, bx, cx, f, niter = 25, f.extra = NA, 
     tol = 1e-05, gridx=NA) 
 
imagePlotInfo (...,breaks, nlevel) 
imageplot.info(...)
imageplot.setup(x, add=FALSE,  legend.shrink = 0.9, legend.width = 1, 
horizontal = FALSE, legend.mar=NULL, bigplot = NULL, smallplot = NULL,...)


makeSimulationGrid(mKrigObject, predictionPoints, nx, ny, nxSimulation,
                 nySimulation, gridRefinement, gridExpansion)
makeSimulationGrid2 (fastTpsObject, predictionPointsList,
                               gridRefinement, gridExpansion)


  
minimax.crit (obj, des = TRUE, R) 
 

## S3 method for class 'spatialDesign'
plot(x,...) 
 
## S3 method for class 'interp.surface'
predict(object, loc,...)  

## S3 method for class 'surface'
predict(object, ...) 
## S3 method for class 'surface.default'
predict(object, ...) 
 

## S3 method for class 'spatialDesign'
 print(x,...) 
## S3 method for class 'sreg'
print(x, ...) 
## S3 method for class 'summary.Krig'
 print(x, ...) 
## S3 method for class 'summarySpatialDesign'
 print(x, digits = 4,...) 
## S3 method for class 'summary.sreg'
 print(x, ...) 
 
printGCVWarnings( Table,  method = "all")
 
makePredictionPoints(mKrigObject, nx, ny, predictionPointsList)

multWendlandGrid( grid.list,center, delta, coef, xy = c(1, 2))

qr.q2ty (qr, y) 
 
qr.yq2 (qr, y) 
## S3 method for class 'qsreg'
plot(x, pch = "*", main = NA,...) 
## S3 method for class 'qsreg'
predict(object, x, derivative = 0, model = object$ind.cv.ps,...)
## S3 method for class 'qsreg'
 print(x, ...) 
qsreg.fit (x, y, lam, maxit = 50, maxit.cv = 10, 
           tol = 1e-04, offset = 0, sc = sqrt(var(y)) * 1e-07, alpha = 0.5, 
           wt = rep(1, length(x)), cost = 1)
qsreg.psi( r,alpha=.5,C=1) 
qsreg.rho( r,alpha=.5,C=1)
qsreg.trace(x, y, lam, maxit = 50, maxit.cv = 10, tol = 1e-04, 
             offset = 0, sc = sqrt(var(y)) * 1e-07, alpha = 0.5, 
             wt = rep(1, length(x)), cost = 1)
qsreg.rho.OLD(r, alpha = 0.5, C = 1)
qsreg.psi.OLD(r, alpha = 0.5, C = 1)

quickPrint(obj, max.values = 10)



radbas.constant (m, d) 
 
sreg.df.to.lambda (df, x, wt, guess = 1, tol = 1e-05) 
sreg.fdf (h, info) 
sreg.fgcv (lam, obj) 
sreg.fgcv.model (lam, obj) 
sreg.fgcv.one (lam, obj) 
sreg.fit (lam, obj, verbose=FALSE) 
sreg.fs2hat (lam, obj) 
sreg.trace (h, info) 

summaryGCV.Krig(object, lambda, cost = 1, verbose = FALSE, 
                offset = 0, y = NULL, ...)
summaryGCV.sreg (object, lambda, cost = 1, nstep.cv = 20, 
        offset = 0, verbose = TRUE,...) 

## S3 method for class 'qsreg'
 summary(object, ...)     
## S3 method for class 'spatialDesign'
 summary(object, digits = 4, ...) 
## S3 method for class 'sreg'
 summary(object, digits = 4, ...) 

surface(object , ...) 
## Default S3 method:
 surface(object, ...) 


unscale (x, x.center, x.scale) 

MLESpatialProcess.fast(x, y, lambda.start=.5, theta.start = NULL, 
  cov.function = "stationary.cov", 
  cov.args = list(Covariance = "Matern", smoothness = 1), 
  Distance = "rdist", verbose=FALSE, optim.args=NULL, ...)