Version increased to 1.0.0 to reflect publication of Youngman (2022, JSS, ).
References to Youngman (2022, JSS, ) 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"
.