devtools::install_github(
  "FinnishCancerRegistry/directadjusting@release"
)

# suppose we have poisson rates that we want to adjust for by age group.
# they are stratified by sex.
set.seed(1337)
offsets <- rnorm(8, mean = 1000, sd = 100)
baseline <- 100
sex_hrs <- rep(1:2, each = 4)
age_group_hrs <- rep(c(0.75, 0.90, 1.10, 1.25), times = 2)
counts <- rpois(8, baseline * sex_hrs * age_group_hrs)

# raw estimates
my_stats <- data.table::data.table(
  sex = rep(1:2, each = 4),
  ag = rep(1:4, times = 2),
  e = counts / offsets
)
my_stats[, "v" := my_stats[["e"]] / offsets]
print(my_stats)
#      sex    ag          e            v
#    <int> <int>      <num>        <num>
# 1:     1     1 0.08928141 8.759527e-05
# 2:     1     2 0.10054601 1.175523e-04
# 3:     1     3 0.11987410 1.238776e-04
# 4:     1     4 0.09722692 8.365551e-05
# 5:     2     1 0.18043221 1.937844e-04
# 6:     2     2 0.14781448 1.227479e-04
# 7:     2     3 0.21747515 1.987203e-04
# 8:     2     4 0.21519746 1.781152e-04

# adjusted by age group
my_adj_stats <- directadjusting::directly_adjusted_estimates(
  stats_dt = my_stats,
  stat_col_nms = "e",
  var_col_nms = "v",
  conf_lvls = 0.95,
  conf_methods = "log",
  stratum_col_nms = "sex",
  adjust_col_nms = "ag",
  weights = c(200, 300, 400, 100)
)

print(my_adj_stats)
# Key: <sex>
#      sex         e            v       e_lo      e_hi
#    <int>     <num>        <num>      <num>     <num>
# 1:     1 0.1056924 3.474049e-05 0.09474912 0.1178996
# 2:     2 0.1889406 5.237509e-05 0.17527556 0.2036710