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⚡ Bolt: Optimize ifelse overhead in glm binomial likelihood calculations #112

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⚡ Bolt: Optimize ifelse overhead in glm binomial likelihood calculations #112

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⚡ Bolt: Optimize ifelse overhead in glm binomial likelihood calculations
seonghobae Aug 31, 2026
b9cfec3
⚡ Bolt: Optimize ifelse overhead in glm binomial likelihood calculations
seonghobae Aug 31, 2026
a69bc8a
⚡ Bolt: Optimize ifelse overhead in glm binomial likelihood calculations
seonghobae Aug 31, 2026
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15 changes: 12 additions & 3 deletions R/llcont.R
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Original file line number Diff line number Diff line change
Expand Up @@ -51,14 +51,23 @@ llcont.glm <- function(x, ...){
switch(fam,
binomial = {
if(is.matrix(y)) {
## Bolt: replaced apply(..., 1, sum) with optimized rowSums() for performance
n <- rowSums(y)
y <- ifelse(n == 0, 0, y[, 1]/n)
## Bolt: replaced ifelse with mathematical operations and logical subsetting for performance
y_opt <- y[, 1]/n
cond_n <- n == 0
cond_n[is.na(cond_n)] <- FALSE
y_opt[cond_n] <- 0
y <- y_opt
} else {
n <- rep.int(1, length(y))
}
m <- if (any(n > 1)) n else wt
wt <- ifelse(m > 0, (wt/m), 0)
## Bolt: replaced ifelse with mathematical operations and logical subsetting for performance
wt_opt <- wt/m
cond_m <- m <= 0
cond_m[is.na(cond_m)] <- FALSE
wt_opt[cond_m] <- 0
wt <- wt_opt
Comment on lines +56 to +70

@devin-ai-integration devin-ai-integration Bot Aug 31, 2026

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🔍 Boundary equivalence lacks regression coverage

No test exercises y_opt with zero trials or wt_opt with zero weights. These boundaries drive the new overwrite logic.

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dbinom(round(m * y), round(m), mpreds, log = TRUE) * wt
},
quasibinomial = {
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