R Under development (unstable) (2024-06-17 r86768 ucrt) -- "Unsuffered Consequences"
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> library(epigrowthfit)
> options(warn = 2L, error = if (interactive()) recover)
> example("egf", package = "epigrowthfit"); o.1 <- m1; o.2 <- m2

egf> ## Simulate 'N' incidence time series exhibiting exponential growth
egf> set.seed(180149L)

egf> N <- 10L

egf> f <- function(time, r, c0) {
egf+     lambda <- diff(exp(log(c0) + r * time))
egf+     c(NA, rpois(lambda, lambda))
egf+ }

egf> time <- seq.int(0, 40, 1)

egf> r <- rlnorm(N, -3.2, 0.2)

egf> c0 <- rlnorm(N, 6, 0.2)

egf> data_ts <-
egf+     data.frame(country = gl(N, length(time), labels = LETTERS[1:N]),
egf+                time = rep.int(time, N),
egf+                x = unlist(Map(f, time = list(time), r = r, c0 = c0)))

egf> rm(f, time)

egf> ## Define fitting windows (here, two per time series)
egf> data_windows <-
egf+     data.frame(country = gl(N, 1L, 2L * N, labels = LETTERS[1:N]),
egf+                wave = gl(2L, 10L),
egf+                start = c(sample(seq.int(0, 5, 1), N, TRUE),
egf+                          sample(seq.int(20, 25, 1), N, TRUE)),
egf+                end = c(sample(seq.int(15, 20, 1), N, TRUE),
egf+                        sample(seq.int(35, 40, 1), N, TRUE)))

egf> ## Estimate the generative model
egf> m1 <-
egf+     egf(model = egf_model(curve = "exponential", family = "pois"),
egf+         formula_ts = cbind(time, x) ~ country,
egf+         formula_windows = cbind(start, end) ~ country,
egf+         formula_parameters = ~(1 | country:wave),
egf+         data_ts = data_ts,
egf+         data_windows = data_windows,
egf+         se = TRUE)
computing a Hessian matrix ...

egf> ## Re-estimate the generative model with:
egf> ## * Gaussian prior on beta[1L]
egf> ## * LKJ prior on all random effect covariance matrices
egf> ##   (here there happens to be just one)
egf> ## * initial value of 'theta' set explicitly
egf> ## * theta[3L] fixed at initial value
egf> m2 <-
egf+     update(m1,
egf+            formula_priors = list(beta[1L] ~ Normal(mu = -3, sigma = 1),
egf+                                  Sigma ~ LKJ(eta = 2)),
egf+            init = list(theta = c(log(0.5), log(0.5), 0)),
egf+            map = list(theta = 3L))
computing a Hessian matrix ...
> 
> 
> ## object ##############################################################
> 
> o.2c <- coef(o.2, random = TRUE)
> o.2c.e <- structure(as.double(o.2c),
+                     len = c(beta = 2L, theta = 2L, b = 40L),
+                     map = list(beta = NULL, theta = c(1L, 2L, NA), b = NULL),
+                     class = "coef.egf")
> stopifnot(identical(o.2c, o.2c.e))
> 
> 
> ## print ###############################################################
> 
> vv <- withVisible(print(o.2c))
       beta        beta       theta       theta           b           b 
-3.10311755  6.42721459 -1.79201534 -0.56685809 -0.59279576  0.12022154 
          b           b           b           b           b           b 
-0.73984082 -0.21830228  0.65420857 -1.23075773  1.04189983 -0.43572373 
          b           b           b           b           b           b 
-0.13138401 -0.92817953  0.27140046 -1.32151717 -0.68686799 -1.49778693 
          b           b           b           b           b           b 
-0.21594208 -1.02836446 -0.44008433 -0.45215250 -0.45694203 -0.62660402 
          b           b           b           b           b           b 
 0.64146636  1.75115488  0.65667882  1.36907332  0.50463209  0.65624489 
          b           b           b           b           b           b 
 1.70208514  1.50944790  0.23078312  0.58900493 -0.19751288 -0.06937314 
          b           b           b           b           b           b 
-0.49636287 -0.22618172 -0.34614308  0.58402472 -1.00809601  1.14565468 
          b           b 
 0.55412319  0.06316734 
> stopifnot(exprs = {
+ 	identical(vv[["value"]], o.2c)
+ 	identical(vv[["visible"]], FALSE)
+ })
> 
> 
> ## as.list #############################################################
> 
> nms <- c("beta", "theta", "b")
> o.2cl <- as.list(o.2c)
> o.2cl.e <- split(o.2c, factor(labels(o.2c), levels = nms))
> for (s in nms)
+ 	attr(o.2cl.e[[s]], "map") <- attr(o.2c, "map")[[s]]
> stopifnot(identical(o.2cl, o.2cl.e))
> 
> proc.time()
   user  system elapsed 
   4.79    0.26    5.03