Skip to contents

Changelog

Source: NEWS.md

ef 2.0.0 (2026-10-09)

  • 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 (2020-10-18)

CRAN release: 2020-11-03

  • move likelihood computation to TMB
  • introduce random effects at the sample level

ef 1.1.0 (2016-08-01)

  • initial package developed using STAN for likelihood computations