# Boost Math - Interpolation

## Interpolation

The Interpolation section of the Boost Math library provides methods for numerical integration and differentiation of functions. These methods can be used directly in R without needing any additional compilation.

### Cardinal Cubic B-Spline

    y <- c(1, 2, 0, 2, 1)
    t0 <- 0
    h <- 1
    spline_obj <- cardinal_cubic_b_spline(y, t0, h)
    x <- 0.5
    spline_obj$spline(x)
    #> [1] 2.895833
    spline_obj$prime(x)
    #> [1] 0.125
    spline_obj$double_prime(x)
    #> [1] -11.16667

### Barycentric Rational Interpolation

    x <- c(0, 1, 2, 3)
    y <- c(1, 2, 0, 2)
    order <- 3
    interpolator <- barycentric_rational(x, y, order)
    xi <- 1.5
    interpolator$interpolate(xi)
    #> [1] 0.9375
    interpolator$derivative(xi)
    #> [1] -2.291667

### Bezier Polynomial

    control_points <- list(c(0, 0, 0), c(1, 2, 0), c(2, 0, 0), c(3, 3, 0))
    interpolator <- bezier_polynomial(control_points)
    xi <- 1.5
    interpolator$spline(xi)
    #> [1] NaN NaN NaN
    interpolator$prime(xi)
    #> [1]  3.00 30.75  0.00
    new_control_point <- c(1.5, 1, 0)
    interpolator$edit_control_point(new_control_point, 2)
    #> NULL

### Bilinear Uniform

    x <- seq(0, 1, length.out = 10)
    interpolator <- bilinear_uniform(x, rows = 2, cols = 5)
    xi <- 0.5
    yi <- 0.5
    interpolator$spline(xi, yi)
    #> [1] 0.3333333

### Cardinal Quadratic B-Spline

    y <- c(0, 1, 0, 1)
    t0 <- 0
    h <- 1
    interpolator <- cardinal_quadratic_b_spline(y, t0, h)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.7857143
    interpolator$prime(xi)
    #> [1] 1.142857

### Cardinal Quintic B-Spline

    y <- seq(0, 1, length.out = 20)
    t0 <- 0
    h <- 1
    interpolator <- cardinal_quintic_b_spline(y, t0, h)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.02631579
    interpolator$prime(xi)
    #> [1] 0.05263158
    interpolator$double_prime(xi)
    #> [1] 4.358112e-16

### Catmull-Rom

    control_points <- list(c(0, 0, 0), c(1, 1, 0), c(2, 0, 0), c(3, 1, 0))
    interpolator <- catmull_rom(control_points)
    xi <- 1.5
    interpolator$spline(xi)
    #> [1] 1.2613446 0.8307972 0.0000000
    interpolator$prime(xi)
    #> [1]  0.8408964 -1.1363078  0.0000000
    interpolator$max_parameter()
    #> [1] 3.567621
    interpolator$parameter_at_point(2)
    #> [1] 2.378414

### Cubic Hermite

    x <- c(0, 1, 2)
    y <- c(0, 1, 0)
    dydx <- c(1, 0, -1)
    interpolator <- cubic_hermite(x, y, dydx)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.625
    interpolator$prime(xi)
    #> [1] 1.25
    interpolator$push_back(3, 0, 1)
    #> NULL
    interpolator$domain()
    #> [1] 0 3

### Cardinal Cubic Hermite

    y <- c(0, 1, 0)
    dydx <- c(1, 0, -1)
    interpolator <- cardinal_cubic_hermite(y, dydx, 0, 1)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.625
    interpolator$prime(xi)
    #> [1] 1.25
    interpolator$domain()
    #> [1] 0 2

### Modified Akima

    x <- c(0, 1, 2, 3)
    y <- c(0, 1, 0, 1)
    interpolator <- makima(x, y)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.7291667
    interpolator$prime(xi)
    #> [1] 1.208333
    interpolator$push_back(4, 1)
    #> NULL

### PCHIP

    x <- c(0, 1, 2, 3)
    y <- c(0, 1, 0, 1)
    interpolator <- pchip(x, y)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.625
    interpolator$prime(xi)
    #> [1] 1.25
    interpolator$push_back(4, 1)
    #> NULL

### Quintic Hermite

    x <- c(0, 1, 2)
    y <- c(0, 1, 0)
    dydx <- c(1, 0, -1)
    d2ydx2 <- c(0, -1, 0)
    interpolator <- quintic_hermite(x, y, dydx, d2ydx2)
    xi <- 0.5
    interpolator$spline(xi)
    #> [1] 0.640625
    interpolator$prime(xi)
    #> [1] 1.40625
    interpolator$double_prime(xi)
    #> [1] -1.25
    interpolator$push_back(3, 0, 1, 0)
    #> NULL
    interpolator$domain()
    #> [1] 0 3