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- `plyr::rbind.fill(...)` → `data.table::rbindlist(..., fill = TRUE, use.names = TRUE)` - `plyr::ddply(dat, cols, func)` → `lapply()` over unique combinations + `data.table::rbindlist()` - Added `data.table::setDF()` calls to convert back to data.frame for compatibility
**1. `beta()` function:** - Replaced growing vector (`beta <- NULL; beta[length(beta) + 1] <- ...`) with pre-allocated `numeric(length(phi))` - Used `which.max(densityIV$y)` instead of `densityIV$y == max(densityIV$y)` (safer, avoids floating-point equality issues) **2. `boot()` function:** - Pre-generate all random values as two matrices (2 `rnorm` calls instead of 2000) - Pre-compute `ones <- rep(1, n)` and `weights` outside the loop instead of recomputing each iteration - Cache `nphi <- length(phi)` to avoid repeated `length()` calls The loop itself still iterates 1000 times calling `beta()` (which calls `stats::density()`), but the per-iteration overhead is reduced by eliminating random number generation and redundant computations from inside the loop
- **Moved `ve` computation outside the loop** — was being recalculated identically on every iteration - **Eliminated the `for` loop entirely** — the `prop` calculation, `vg`, `r`, `correction`, and `sign` operations are all vectorized now - **Single call to `get_population_allele_frequency()`** with full vectors instead of element-by-element
- Pre-generate all random values as two matrices (2 `rnorm` calls instead of 2000) — `boot_exp` and `boot_out` are `nboot x nsnp` matrices - Loop now indexes rows from the pre-generated matrices instead of calling `rnorm()` each iteration - `sapply` → `vapply` for `model`, `Q`, `Qdash` extraction (4 calls)
- sapply` → `vapply` for `model`, `Q`, `Qdash` extraction (4 calls)
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Some optimizations including:
mr_egger_regression_bootstrap()weighted_median_bootstrap()weighted_median()functionplyr::rbind.fill(...)todata.table::rbindlist(..., fill = TRUE, use.names = TRUE)plyr::ddply(dat, cols, func)tolapply()over unique combinations +data.table::rbindlist()data.table::setDF()calls to convert back to data.frame for compatibilityflip_alleles()usechartr()instead of 4gsub()callsrandom_string()use single call tosample()instead of n callsmr_mode()apply(..., any(is.na()))withcomplete.case()mr()functionOptimize get_r_from_lor()functionmr_rucker_bootstrap()andmr_rucker_jackknife_internal()functionssapply()withvapply()in several casessimple_cap()function