CurveFit.jl

CurveFit provides a unified and extensible interface for linear, nonlinear, and specialized curve fitting in Julia. It offers built-in solvers for common linear and special-function models, while general nonlinear curve fitting is handled through nonlinear least squares methods from NonlinearSolve.jl.

Curve fitting problems are defined in a consistent problem–solver style, allowing flexible solver selection and access to common statistical diagnostics such as residuals, standard errors, and confidence intervals via the StatsAPI.jl interface.

Installation

using Pkg
Pkg.add("CurveFit")

Quick start

using CurveFit

# Sample data
x = 0:0.1:10
y = @. 2x + 1

# Create and solve the problem
prob = CurveFitProblem(x, y)
sol = solve(prob, LinearCurveFitAlgorithm())

# Check the fitted coefficients
println("Slope (a): ", sol.u[1])
println("Intercept (b): ", sol.u[2])

# Make predictions
println("Prediction at x=5: ", sol(5.0))

Slope (a): 2.0
Intercept (b): 1.0
Prediction at x=5: 11.0

See Getting started for more step-by-step examples with common fits.