devtools::install_github("Non-Contradiction/autodiffr")

devtools::install_github("Non-Contradiction/JuliaCall")

library(autodiffr)

## Do initial setup

ad_setup()
#> Julia version 1.0.0 at location /Applications/Julia-1.0.app/Contents/Resources/julia/bin will be used.
#> Loading setup script for JuliaCall...
#> Finish loading setup script for JuliaCall.

## If you want to use a julia at a specific location, you could do the following:
## ad_setup(JULIA_HOME = "the folder that contains julia binary"), 
## or you can set JULIA_HOME in command line environment or use `options(...)`

## Define a target function with vector input and scalar output
f <- function(x) sum(x^2L)

## Calculate gradient of f at [2,3] by
ad_grad(f, c(2, 3)) ## deriv(f, c(2, 3))
#> [1] 4 6

## Get a gradient function g
g <- makeGradFunc(f)

## Evaluate the gradient function g at [2,3]
g(c(2, 3))
#> [1] 4 6

## Calculate hessian of f at [2,3] by
ad_hessian(f, c(2, 3))
#>      [,1] [,2]
#> [1,]    2    0
#> [2,]    0    2

## Get a hessian function h
h <- makeHessianFunc(f)

## Evaluate the hessian function h at [2,3]
h(c(2, 3))
#>      [,1] [,2]
#> [1,]    2    0
#> [2,]    0    2

## Define a target function with vector input and vector output
f <- function(x) x^2

## Calculate jacobian of f at [2,3] by
ad_jacobian(f, c(2, 3))
#>      [,1] [,2]
#> [1,]    4    0
#> [2,]    0    6

## Get a jacobian function j
j <- makeJacobianFunc(f)

## Evaluate the gradient function j at [2,3]
j(c(2, 3))
#>      [,1] [,2]
#> [1,]    4    0
#> [2,]    0    6

## Define a target function with mulitple arguments
f <- function(a = 1, b = 2, c = 3) a * b ^ 2 * c ^ 3

## Calculate gradient/derivative of f at a = 2, when b = c = 1 by
ad_grad(f, 2, b = 1, c = 1) ## deriv(f, 2, b = 1, c = 1)
#> [1] 1

## Get a gradient/derivative function g w.r.t a when b = c = 1 by
g <- makeGradFunc(f, b = 1, c = 1)

## Evaluate the gradient/derivative function g at a = 2
g(2)
#> [1] 1

## Calculate gradient/derivative of f at a = 2, b = 3, when c = 1 by
ad_grad(f, list(a = 2, b = 3), c = 1)
#> $a
#> [1] 9
#> 
#> $b
#> [1] 12

## Get a gradient/derivative function g w.r.t a and b when c = 1 by
g <- makeGradFunc(f, c = 1)

## Evaluate the gradient/derivative function g at a = 2, b = 3
g(list(a = 2, b = 3))
#> $a
#> [1] 9
#> 
#> $b
#> [1] 12