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.1s <- summary(o.1)
> o.1f <- fitted(o.1, class = TRUE, se = TRUE)
> stopifnot(exprs = {
+ 	is.list(o.1s)
+ 	identical(oldClass(o.1s), "summary.egf")
+ 	length(o.1s) == 5L
+ 	identical(names(o.1s), c("fitted", "convergence", "value", "gradient", "hessian"))
+ 	identical(o.1s[["convergence"]], o.1[["optimizer_out"]][["convergence"]])
+ 	identical(o.1s[["value"]], o.1[["value"]])
+ 	identical(o.1s[["gradient"]], o.1[["gradient"]])
+ 	identical(o.1s[["hessian"]], o.1[["hessian"]])
+ 	all.equal(o.1s[["fitted"]], simplify2array(c(tapply(o.1f[["value"]], o.1f[["top"]], summary))))
+ })
> 
> 
> ## print ###############################################################
> 
> vv <- withVisible(print(o.1s))
Fitted values ..........................................................

             log(r)  log(c0)
  Min.    -3.280004 5.569983
  1st Qu. -3.180285 6.026158
  Median  -3.127046 6.347277
  Mean    -3.094246 6.418744
  3rd Qu. -3.006118 6.773228
  Max.    -2.818894 7.424955

Negative log marginal likelihood .......................................

  convergence          0                        
  value                1.032019e+03             
  range(abs(gradient)) 5.675084e-05 1.625871e-03
  pos. def. Hessian    TRUE                     
> stopifnot(exprs = {
+ 	identical(vv[["value"]], o.1s)
+ 	identical(vv[["visible"]], FALSE)
+ })
> 
> proc.time()
   user  system elapsed 
   4.87    0.31    5.15