⚡ Bolt: performance improvement

.jules/bolt.md

@@ -12,3 +12,6 @@
 ## 2026-07-14 - Matrix Cross Product Optimization
 **Learning:** In R, matrix multiplication of the form `t(X) %*% Y` explicitly allocates memory for the transposed matrix. Using the optimized base function `crossprod(X, Y)` avoids this allocation.
 **Action:** Always replace `t(X) %*% Y` with `crossprod(X, Y)` for faster and more memory-efficient cross-product calculations.
+## 2026-10-31 - ifelse Evaluation Overhead
+**Learning:** In R codebases, `ifelse` evaluates both true and false branches entirely before subsetting, and drops vector names and attributes which creates overhead.
+**Action:** Optimize `ifelse` by using explicitly preallocated assignments like `res <- Y * 0` combined with subsetting `res[cond] <- ...` to significantly boost performance.

R/llcont.R

@@ -150,23 +150,40 @@ llcont.hurdle <- function(x, ...) {
   zeroPoisson <- function(parms) {
     mu <- as.vector(exp(Z %*% parms + offsetz))
     loglik0 <- -mu
-    Y0 * weights * loglik0 + ifelse(Y1, weights * log(1 - exp(loglik0)), 0)
+    ## Bolt: replaced ifelse with explicitly preallocated vector assignment for performance
+    res <- Y * 0
+    if (any(Y1)) {
+      w_sub <- if (length(weights) == 1) weights else weights[Y1]
+      res[Y1] <- w_sub * log(1 - exp(loglik0[Y1]))
+    }
+    Y0 * weights * loglik0 + res
   }
 
   countPoisson <- function(parms) {
     mu <- Y1 * as.vector(exp(X %*% parms + offsetx))
     loglik0 <- -mu
     loglik1 <- Y1 * dpois(Y, lambda = mu, log = TRUE)
-    Y1 * weights * loglik1 - ifelse(Y1, weights * log(1 - exp(loglik0)), 0)
+    ## Bolt: replaced ifelse with explicitly preallocated vector assignment for performance
+    res <- Y * 0
+    if (any(Y1)) {
+      w_sub <- if (length(weights) == 1) weights else weights[Y1]
+      res[Y1] <- w_sub * log(1 - exp(loglik0[Y1]))
+    }
+    Y1 * weights * loglik1 - res
   }
 
   zeroNegBin <- function(parms) {
     mu <- as.vector(exp(Z %*% parms[1:kz] + offsetz))
     theta <- exp(parms[kz + 1])
     loglik0 <- suppressWarnings(dnbinom(0, size = theta,
                                         mu = mu, log = TRUE))
-    Y0 * weights * loglik0 +
-        ifelse(Y1, weights * log(1 - exp(loglik0)), 0)
+    ## Bolt: replaced ifelse with explicitly preallocated vector assignment for performance
+    res <- Y * 0
+    if (any(Y1)) {
+      w_sub <- if (length(weights) == 1) weights else weights[Y1]
+      res[Y1] <- w_sub * log(1 - exp(loglik0[Y1]))
+    }
+    Y0 * weights * loglik0 + res
   }
 
   countNegBin <- function(parms) {
@@ -176,7 +193,13 @@ llcont.hurdle <- function(x, ...) {
                                         mu = mu, log = TRUE))
     loglik1 <- suppressWarnings(dnbinom(Y, size = theta,
                                         mu = mu, log = TRUE))
-    ifelse(Y1, weights * loglik1 - weights * log(1 - exp(loglik0)), 0)
+    ## Bolt: replaced ifelse with explicitly preallocated vector assignment for performance
+    res <- Y * 0
+    if (any(Y1)) {
+      w_sub <- if (length(weights) == 1) weights else weights[Y1]
+      res[Y1] <- w_sub * loglik1[Y1] - w_sub * log(1 - exp(loglik0[Y1]))
+    }
+    res
     }
 
   zeroGeom <- function(parms) zeroNegBin(c(parms, 0))