The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

gslnls 1.4.2

• confint(), summary() and other methods no longer fail in case of a singular gradient
• Fixed bug: missing confidence/prediction intervals in predict() for multi-start gsl_nls() call with fn defined as a function in combination with newdata.

gslnls 1.4.1

• Fixed compatibility GSL versions < 2.5

gslnls 1.4.0

• Robust loss optimization added in gsl_nls() via argument loss
  
• weights in gsl_nls() accepts a matrix (in addition to a vector) in which case the objective function is generalized least squares
• Added new function gsl_nls_loss()
• Added new method cooks.distance()
• Minor changes in predict() and hatvalues() for weighted NLS

gslnls 1.3.3

• Fix standard errors predict() when using newdata

gslnls 1.3.2

• Reverted to static Makevars.win (supplied by T. Kalibera)
• Added new method hatvalues()

gslnls 1.3.1

• Minor edits configure.ac to fix cran check results

gslnls 1.3.0

• Missing starting values/ranges allowed in gsl_nls()
• lower and upper parameter constraints included in gsl_nls()
• Added 3 regression problems from Bates & Watts (1988)
• Updated multi-start algorithm in gsl_nls()
• Added configure.win, cleanup.win and Makevars.win.in
• Removed old Makevars and Makevars.win
• Several minor changes

gslnls 1.2.0

• Added multi-start algorithm to gsl_nls()
• Added 56 NLS regression and optimization test problems
• Added unit tests in folder unit_tests
• Several minor changes/fixes

gslnls 1.1.1

• Clean exits gsl_nls() and gsl_nls_large() when interrupted
• Default algorithm in gsl_nls_large() set to "lm"

gslnls 1.1.0

• Added large-scale NLS regression with gsl_nls_large()

gslnls 1.0.2

• Added Makevars.ucrt

gslnls 1.0.1

• Initial release

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.