Following up on a remark by Roger below, I am not so sure that the licence of akima excludes using it for paid research.

The LICENCE file of the package refers to this webpage about the ACM licence:

http://www.acm.org/publications/policies/softwarecrnotice

Noncommercial use is defined as "academic, research and other similar noncommercial uses". Note the comma. "Research use" is allowed under the current licence. 

"Commercial use" is restricted but clearly refers to selling software containing akima code, not to any "research use".

Jacob.

--- On Sun, 5/12/10, Robert J. Hijmans <r.hijmans@gmail.com> wrote:

From: Robert J. Hijmans <r.hijmans@gmail.com>
Subject: Re: [R-sig-Geo] Polygon dissolution or union issue
To: "Roger.Bivand" <Roger.Bivand@nhh.no>
Cc: "Sébastien Durand" <v8extra@gmail.com>, "r-sig-geo" <r-sig-geo@r-project.org>
Date: Sunday, 5 December, 2010, 23:05

What is the value of the interp function in Akima? I have noticed on
this list that people use it, but I do not why. Is the Tps (thin-plate
spline) method in Fields a sufficient (or better) alternative? In a
comparison using these data (sampling only a few points to 'train' the
interpolator, to make it non-trivial), Tps does _much_ better than
interp in Akima. But perhaps that is different with different
settings. or with different data. Or is interp more efficient with
large datasets? Or?

library(akima)
library(maptools)
gpclibPermit()
library(raster)
library(fields)

data(akima)
akimadf <- data.frame(x=akima$x, y=akima$y, z=akima$z)
dimgrid <- 50

tpcor=akcor=list()

for (i in 1:10) {
  s <- sample(50, 40)
  train <- akimadf[-s,]
  test <- akimadf[s,]
  ak.li <- interp(train$x, train$y, train$z, xo=seq(min(akima$x),
max(akima$x), length = dimgrid), yo=seq(min(akima$y), max(akima$y),
length = dimgrid),linear=FALSE, extrap=TRUE, duplicate = "mean")
  r <- raster(ak.li)
  tps <- Tps(train[,1:2], train[,'z'])
  p <- interpolate(r, tps)
  ak <- extract(r, test[,1:2])
  tp <- extract(p, test[,1:2])
  # correlation between predicted and withheld values
  akcor[i] <- cor(ak, test[,3])
  tpcor[i] <- cor(tp, test[,3])
}
mean(unlist(akcor))  # akima:interp
mean(unlist(tpcor))   # fields::Tps


A typical result:
> mean(unlist(akcor))
[1] 0.3952623
> mean(unlist(tpcor))
[1] 0.9307129
>



On Sun, Dec 5, 2010 at 10:59 AM, Roger Bivand <Roger.Bivand@nhh.no> wrote:
> On Sat, 4 Dec 2010, Rob


      
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