Changes in version 1.0.2                        

Changes:

  - Added function and class geoevgam with S3 method plot for fitting
    and plotting geometric extreme value models. (Thanks to Callum
    Murphy-Barltrop, Jenny Wadsworth and Miguel de Carvalho.)

  - Added type = 'qqplot2' to predict() for residual-based QQ-plots.
    (Thanks to Jenny Wadsworth.)

  - Added family = 'gw' for fitting generalised Weibull distribution.
    (Thanks to Cees de Valk.)

  - Added family = 'poisson' for fitting Poisson distribution.

  - Added args(dist = 'aggauss') for fitting family = 'condex' with
    asymmetric generalised Gaussian residuals. (Thanks again to Kristina
    Bratkova and Aiden Farrell.)

  - Added family = 'aggauss' for fitting asymmetric generalised Gaussian
    distribution. (Thanks to Kristina Bratkova and Aiden Farrell.)

  - GPD model with shape parameter constrained to [-0.5, 1.0] added with
    family = "gpd2".

  - Added ltgamma and ltgammab families for the left truncated gamma
    distribution with unknown and know shape, respectively. Use args =
    list(lower = ) to give the scalar, vector or matrix of
    left-truncation points, and args = list(alpha = ) to give scalar,
    vector or matrix of gamma distribution shape parameters with family
    ltgammab.

  - Added condex, beta, logitgauss families.

  - Added option sparse = TRUE, which coerces matrices to sparse
    matrices through package Matrix where possible.

  - Added df2matdf() for turning a vector response to a matrix response
    if explanatory variable combinations are repeated.

  - Added functionality to fit extended generalised Pareto distribution
    through evgam(..., family = "egpd"). See Naveau et al. (Water
    Resour. Res., 2016, (https://doi.org/10.1002/2015WR018552)) and
    family.evgam.

  - Added functionality to fit blended generalised extreme value (GEV)
    distribution through evgam(..., family = "bgev"). See Naveau et al.
    (Water Resour. Res., 2016, (https://doi.org/10.1002/2015WR018552))
    and family.evgam. (Thanks to Jordan Richards for the suggestion.)

  - Also added dbgev(), pbgev(), qbgev() and rbgev() for density,
    distribution function, quantile function and random generation,
    respectively, for the blended GEV distribution.

  - Added functionality to fit models via custom likelihood functions,
    i.e. extending those available in evgam through family = .... See
    custom.family.evgam.

  - Added functionality to constrain both GPD parameters using gpd.args
    = list(lower = ..., upper = ...). (Thanks to Callum Murphy-Barltrop
    for the suggestion.)

  - GEV model with shape parameter constrained to [-0.5, 1.0] added with
    family = "gev2".

Bug fixes:

  - That only variables are checked as being supplied to data is now
    properly detected. (Thanks, Simon Brown.)

  - That values smoothing parameters supplied to evgam() are properly
    recognised has been fixed.

                 Changes in version 1.0.1 (2025-09-23)                  

Changes:

  - Makevars and Makevars.win updated.

                 Changes in version 1.0.0 (2022-06-28)                  

Changes:

  - 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.

Bug fixes:

  - That all variables have been supplied to data is now properly
    detected.

                 Changes in version 0.1.4 (2020-08-26)                  

Changes:

  - None.

Bug fixes:

  - 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.)

                        Changes in version 0.1.3                        

Changes:

  - 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).

                 Changes in version 0.1.2 (2020-04-18)                  

Changes:

  - 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.

Bug fixes:

  - 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).

                 Changes in version 0.1.1 (2020-03-15)                  

Changes:

  - plot.evgam() now has informative y-axis labels for one-dimensional
    smooths.

Bug fixes:

  - Compilation flag with clang++ in gradHess.cpp addressed.

  - simulate.evgam() correctly labels variables for family = "response".

                 Changes in version 0.1.0 (2020-03-08)                  

  - Initial release.