NEWS
ef 2.0.0
- move likelihood computation to RTMB, the package no longer has compiled code
- random effects can be included using
s(f, bs = "re") terms in the model
formula, for example for between visit and between sample variation
- remove
overdispersion() and BICadj(), these are replaced by visit and
sample random effects
- remove the
sample_re argument of efp()
- smooth terms are now penalised and the amount of smoothing is estimated,
by fitting smooths as random effects. Previously smooths were unpenalised
regression splines, which are still available using
fx = TRUE, for
example s(x, k = 3, fx = TRUE)
- the recommended smooths are first and second order random walks, using
s(f, bs = "mrf", xt = list(penalty = rw_penalty(levels(f)))). The new
function rw_penalty() creates the penalty, and these are fitted using
sparse matrices so they are quick to fit even with many levels. Other
smooths from mgcv can be used but are slower to fit
- add a
predict() method for efp fits, with predictions for new data,
standard errors and the option to exclude random effects
fitted(type = "pi") now returns the probability of capture in each pass- add day of the year (
doy) to the ef_data example dataset, a continuous
covariate which can be used to fit smooth terms
- add examples of smooth terms to the help and the README (#1)
- add
summary(), vcov(), confint(), logLik() and formula() methods
for efp fits. residuals(), anova() and plot() now give an
informative error, rather than failing in the methods inherited from glm
- remove the unused
init argument of efp()
ef 1.2.0 (2020-11-03)
- move likelihood computation to TMB
- introduce random effects at the sample level
ef 1.1.0
- initial package developed using STAN for likelihood computations