Add an overload for ScalarModel to operate in-place

Also fixed the version number, which was accidentally bumped in a previous PR.

Author: JamesWrigley

Project.toml

@@ -1,6 +1,6 @@
 name = "CurveFit"
 uuid = "5a033b19-8c74-5913-a970-47c3779ef25c"
-version = "1.5.0"
+version = "1.4.0"
 authors = ["Paulo José Saiz Jabardo <pjabardo@gmail.com>, Avik Pal <avikpal@mit.edu> and contributors"]
 
 [deps]

docs/src/_changelog.md

@@ -7,12 +7,16 @@ CurrentModule = CurveFit
 This documents notable changes in CurveFit.jl. The format is based on [Keep a
 Changelog](https://keepachangelog.com).
 
-## [v1.4.0] - 2026-01-30
+## [v1.4.0] - 2026-01-31
 
 ### Added
 - Implemented [`margin_error()`](@ref) ([#81]).
 - Added support for standard deviation weights for linear fits ([#80]).
 
+### Changed
+- [`ScalarModel()`](@ref)'s will now operate in-place for improved performance
+  ([#82]).
+
 ## [v1.3.0] - 2026-01-26
 
 ### Added

src/common_interface.jl

@@ -176,6 +176,7 @@ struct ScalarModel{F}
 end
 
 # When called with array data, broadcast over the data
+(sm::ScalarModel)(out, params, x::AbstractArray) = out .= sm.f.(Ref(params), x)
 (sm::ScalarModel)(params, x::AbstractArray) = sm.f.(Ref(params), x)
 # When called with scalar data (for single-point evaluation), call directly
 (sm::ScalarModel)(params, x::Number) = sm.f(params, x)

test/nonlinfit.jl

@@ -239,7 +239,9 @@ end
 
     # Use ScalarModel wrapper
     prob = NonlinearCurveFitProblem(ScalarModel(fn_scalar), [0.5, 0.5, 0.5], x, y)
+    @test SciMLBase.isinplace(prob.nlfunc)
     sol = solve(prob)
+    @test sol(x) isa Vector
 
     @test sol.u ≈ a0
     @test SciMLBase.successful_retcode(sol.retcode)