Version increased to 1.0.0 to reflect publication of Youngman (2022, JSS, \doi{10.18637/jss.v103.i03}).
References to Youngman (2022, JSS, \doi{10.18637/jss.v103.i03}) added, where appropriate.
data is now properly detected.plot() for an evgam object now calls mgcv::plot.gam() to plot smooths (with thanks to Debbie Dupuis for triggering this). plot() no longer has the addMap option, for adding map outlines via maps::map(); instead using one-figure devices with maps::map() separately is recommended.
Calculations of log(|S|_+) for penalty matrix S now fully implements Wood (JRSSB, 2011(73)1, Appendix B).
Calculations of log(|H|) for Hessian H now use diagonality simpifications; see Wood (book: GAMs in R 2nd ed. (2017) pp. 286).
The Fremantle data from package ismev have been added, and are used for examples. Usage is data(fremantle), as in ismev.
colplot() adds the option to add a legend, which defaults to FALSE.
logLik.evgam() now returns an object of class 'logLik', allowing, e.g., AIC() and BIC() to be used.
extremal0() has gone, as extremal() can now do the same.
evgam()'s trace argument now allows -1, which suppresses any information on the console.
Negative response data now work okay with family = "ald".
evgams()'s formula argument may have smooths and parametric-only terms in any order. (Previously, smooths had
to come first, so formula = list(response ~ s(), ~ 1, ~ s()) broke.)
predict.evgam(object) with missing(newdata) only gave one set predictions for object$data. It now gives predictions
for all rows of object$data (as it should).
plot.evgam() now has informative y-axis labels for one-dimensional smooths.Compilation flag with clang++ in gradHess.cpp addressed.
simulate.evgam() correctly labels variables for family = "response".