Computes the numerical gradient of `functions`

or the symbolic gradient of `characters`

in arbitrary orthogonal coordinate systems.

```
gradient(
f,
var,
params = list(),
coordinates = "cartesian",
accuracy = 4,
stepsize = NULL,
drop = TRUE
)
f %gradient% var
```

- f
array of

`characters`

or a`function`

returning a`numeric`

array.- var
vector giving the variable names with respect to which the derivatives are to be computed and/or the point where the derivatives are to be evaluated. See

`derivative`

.- params
`list`

of additional parameters passed to`f`

.- coordinates
coordinate system to use. One of:

`cartesian`

,`polar`

,`spherical`

,`cylindrical`

,`parabolic`

,`parabolic-cylindrical`

or a vector of scale factors for each varibale.- accuracy
degree of accuracy for numerical derivatives.

- stepsize
finite differences stepsize for numerical derivatives. It is based on the precision of the machine by default.

- drop
if

`TRUE`

, return the gradient as a vector and not as an`array`

for scalar-valued functions.

Gradient vector for scalar-valued functions when `drop=TRUE`

, `array`

otherwise.

The gradient of a scalar-valued function \(F\) is the vector
\((\nabla F)_i\) whose components are the partial derivatives of \(F\)
with respect to each variable \(i\).
The `gradient`

is computed in arbitrary orthogonal coordinate systems using the
scale factors \(h_i\):

$$(\nabla F)_i = \frac{1}{h_i}\partial_iF$$

When the function \(F\) is a tensor-valued function \(F_{d_1,\dots,d_n}\),
the `gradient`

is computed for each scalar component. In particular, it becomes
the Jacobian matrix for vector-valued function.

$$(\nabla F_{d_1,\dots,d_n})_i = \frac{1}{h_i}\partial_iF_{d_1,\dots,d_n}$$

`f %gradient% var`

: binary operator with default parameters.

Guidotti E (2022). "calculus: High-Dimensional Numerical and Symbolic Calculus in R." Journal of Statistical Software, 104(5), 1-37. doi:10.18637/jss.v104.i05

Other differential operators:
`curl()`

,
`derivative()`

,
`divergence()`

,
`hessian()`

,
`jacobian()`

,
`laplacian()`

```
### symbolic gradient
gradient("x*y*z", var = c("x", "y", "z"))
#> [1] "y * z" "x * z" "x * y"
### numerical gradient in (x=1, y=2, z=3)
f <- function(x, y, z) x*y*z
gradient(f = f, var = c(x=1, y=2, z=3))
#> [1] 6 3 2
### vectorized interface
f <- function(x) x[1]*x[2]*x[3]
gradient(f = f, var = c(1, 2, 3))
#> [1] 6 3 2
### symbolic vector-valued functions
f <- c("y*sin(x)", "x*cos(y)")
gradient(f = f, var = c("x","y"))
#> [,1] [,2]
#> [1,] "y * cos(x)" "sin(x)"
#> [2,] "cos(y)" "-(x * sin(y))"
### numerical vector-valued functions
f <- function(x) c(sum(x), prod(x))
gradient(f = f, var = c(0,0,0))
#> [,1] [,2] [,3]
#> [1,] 1 1 1
#> [2,] 0 0 0
### binary operator
"x*y^2" %gradient% c(x=1, y=3)
#> [1] 9 6
```