Migrate parallel computing in as.sir() from parallel:: to future/future.apply (#280)

* Migrate parallel computing in as.sir() from parallel:: to future/future.apply

Replace parallel::mclapply() and parallel::parLapply() with
future.apply::future_lapply(), enabling transparent support for any
future backend (multisession, multicore, mirai_multisession, cluster)
on all platforms including Windows.

When parallel = TRUE the function now: (1) respects an active
future::plan() set by the user without overriding it on exit, or
(2) sets a temporary multisession plan with parallelly::availableCores()
and tears it down on exit. The max_cores argument controls worker count
only when no user plan is active.

future and future.apply are added to Suggests in DESCRIPTION.

https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8

* Require user plan() for parallel=TRUE; fix as_wt_nwt false-positive warnings

- parallel = TRUE now errors with a cli-styled message if no non-sequential
  future::plan() is active; users must call e.g. future::plan(future::multisession)
  before using parallel = TRUE (breaking change)
- Removed auto-setup/teardown of multisession plan inside as.sir(), which was
  slow and caused version-mismatch issues with load_all() workflows
- Added as_wt_nwt to the exclusion list in as_sir_method() to suppress
  false-positive "no longer used" warnings during parallel runs
- Fixed pieces_per_col row-batch calculation to use n_workers (total available
  workers from the active plan) instead of n_cores (workers clipped to n_cols),
  so row-batch mode activates correctly when n_cols < n_workers
- Updated @param parallel and @param max_cores roxygen docs; regenerated man/as.sir.Rd
- Updated sequential-mode hint to instruct users to set plan() first

https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8

* fix parallel

* fix parallel

* unit tests

* unit tedts

---------

Co-authored-by: Claude <noreply@anthropic.com>

R/aa_helper_functions.R

@@ -1681,28 +1681,6 @@ readRDS_AMR <- function(file, refhook = NULL) {
   readRDS(con, refhook = refhook)
 }
 
-get_n_cores <- function(max_cores = Inf) {
-  if (pkg_is_available("parallelly", min_version = "0.8.0", also_load = FALSE)) {
-    available_cores <- import_fn("availableCores", "parallelly")
-    n_cores <- min(available_cores(), na.rm = TRUE)
-  } else {
-    # `parallel` is part of base R since 2.14.0, but detectCores() is not very precise on exotic systems like Docker and quota-set Linux environments
-    n_cores <- parallel::detectCores()[1]
-    if (is.na(n_cores)) {
-      n_cores <- 1
-    }
-  }
-  max_cores <- floor(max_cores)
-  if (max_cores == 0) {
-    n_cores <- 1
-  } else if (max_cores < 0) {
-    n_cores <- max(1, n_cores - abs(max_cores))
-  } else if (max_cores > 0) {
-    n_cores <- min(n_cores, max_cores)
-  }
-  n_cores
-}
-
 # Support `where()` if tidyselect not installed ----
 if (!is.null(import_fn("where", "tidyselect", error_on_fail = FALSE))) {
   # tidyselect::where() exists, retrieve from their namespace to make `where()`s work across the package in default arguments