[R-pkgs] Introducing the futile.paradigm, a package for functional dispatching in R

Nurometic R Help r at nurometic.com
Wed Oct 27 02:48:00 CEST 2010

Hello useRs,

I'm pleased to announce the general availability of the R package
futile.paradigm, which is a language extension that implements
functional dispatching in R. This is an alternative to the current
object-oriented styles, replacing them with a functional programming
style that provides a clean, fine-grained declarative syntax for
function definitions. The core of the package consists of two operators,
%when% and %must% that implement guard statements that define when a
function variant is executed and post assertions that define the
criteria for success on individual functions. The package also
implements custom type constructors, which are declared using the
'create' function, which is predefined in the futile.paradigm.

Here are some examples to give you an idea of the syntax.

# Factorial function in the 'fac' namespace
fac.0 %when% (x == 0)  # guard statement
fac.0 <- function(x) 1 # function variant

fac.n %when% (x > 0)       # guard statement
fac.n %must% (result >= x) # post-assertion
fac.n <- function(x) x * fac(x-1) # function variant

> fac(4)
[1] 24
> fac(-2)
Error in UseFunction("fac", ...) : No valid function for 'fac/1' : -2

# Numerical optimization with custom type constructors
# Define a function and its derivatives
fx <- function(x) x^2 - 4
f1 <- function(x) 2*x
f2 <- function(x) 2

# Define the numerical optimization harness
converged <- function(x1, x0, tolerance=1e-6) abs(x1 - x0) < tolerance
minimize <- function(x0, algo, max.steps=100)
  step <- 0
  old.x <- x0
  while (step < max.steps)
    # Calls abstract function 'iterate' with definitions below
    new.x <- iterate(old.x, algo)
    if (converged(new.x, old.x)) break
    old.x <- new.x

# Implement Newton-Raphson
iterate.nr %when% (algo %isa% NewtonRaphson)
iterate.nr <- function(x, algo) x - algo$f1(x) / algo$f2(x)

# Implement Gradient Descent
iterate.gd %when% (algo %isa% GradientDescent)
iterate.gd <- function(x, algo) x - algo$step * algo$f1(x)

# Create a custom type constructor
create.GradientDescent <- function(T, f1, step=0.01) list(f1=f1,step=step)

> algo <- create(GradientDescent, f1)
> minimize(3, algo)
[1] 3.677989e-06

# Execute using a dynamic type constructor
> algo <- create(NewtonRaphson, f1=f1,f2=f2)
> minimize(3, algo)
[1] 0

More documentation is available in the package help files and also at

The current version is version 1.2.0 and is available on CRAN.

Brian Lee Yung Rowe

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