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

References to Youngman (2022, JSS, ) added, where appropriate.

- That all variables have been supplied to
`data`

is now properly detected.

- None.

- An error is thrown if there are fewer than r data for any pp.args$id, as opposed to r + 1 incorrectly implemented previously. (Thanks, Yousra El Bachir.)

`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"`

.

- Initial release.