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/bug_drug_combinations.R

@@ -129,16 +129,21 @@ bug_drug_combinations <- function(x,
       # turn and merge everything
       pivot <- lapply(x_mo_filter, function(x) {
         m <- as.matrix(table(as.sir(x), useNA = "always"))
+        na_idx <- which(is.na(rownames(m)))
+        get_row <- function(lbl) {
+          idx <- which(rownames(m) == lbl)
+          if (length(idx) == 1L) unname(m[idx, ]) else rep(0L, ncol(m))
+        }
         data.frame(
-          S = m["S", ],
-          SDD = m["SDD", ],
-          I = m["I", ],
-          R = m["R", ],
-          NI = m["NI", ],
-          WT = m["WT", ],
-          NWT = m["NWT", ],
-          NS = m["NS", ],
-          na = m[which(is.na(rownames(m))), ],
+          S = get_row("S"),
+          SDD = get_row("SDD"),
+          I = get_row("I"),
+          R = get_row("R"),
+          NI = get_row("NI"),
+          WT = get_row("WT"),
+          NWT = get_row("NWT"),
+          NS = get_row("NS"),
+          na = if (length(na_idx) == 1L) unname(m[na_idx, ]) else rep(0L, ncol(m)),
           stringsAsFactors = FALSE
         )
       })