Nonlinear least squares examples from NIST
original from National Institutes for Standards and Technology (NIST)
http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml
R port by Douglas Bates <bates@stat.wisc.edu>
Datasets for testing nonlinear regression routines.
The original data sets and descriptions are from the
NIST web site and were not covered by an explicit copyright.
Modifications for R data sets are covered by GPL (>= 2).

original from National Institutes for Standards and Technology
(NIST) http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml R
port by Douglas Bates <bates@stat.wisc.edu>

NISTnls
0.9-2
original from National Institutes for Standards and Technology (NIST)
http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml
R port by Douglas Bates <bates@stat.wisc.edu>
Datasets for testing nonlinear regression routines.
nls
Original pages from NIST web site.  Modifications for R covered by
GPL (version 2 or later)

NISTnls
0.9-3
2000/02/06
Nonlinear least squares examples from NIST
original from National Institutes for Standards and Technology (NIST)
http://www.itl.nist.gov/div898/strd/nls/nls_main.shtml
R port by Douglas Bates <bates@stat.wisc.edu>
Datasets for testing nonlinear regression routines.
nls
Original pages from NIST web site.  Modifications for R covered by
GPL (version 2 or later)

Fundamental Physical Constants and Unit Conversions from NIST
c(person(given = "Jose", family = "Gama", role = c("aut","cre"),email = "rxprtgama@gmail.com"))
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for
SI (International System of Units) and non-SI, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology)

Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for
SI (International System of Units) and non-SI units, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology, USA)

Finds the Best Subset of Points to Sample
Given a few examples of experiments over a time (or spatial) course,
'NITPicker' selects a subset of points to sample in follow-up experiments,
which would (i) best distinguish between the experimental conditions and the
control condition (ii) best distinguish between two models of how the
experimental condition might differ from the control (iii) a combination of
the two. Ezer and Keir (2018) <doi:10.1101/301796>.