---
title: | 
       | Exposing \proglang{C++} functions and classes 
       | with \pkg{Rcpp} modules

# Use letters for affiliations
author:
  - name: Dirk Eddelbuettel
    affiliation: a
  - name: Romain François
    affiliation: b

address:
  - code: a
    address: \url{http://dirk.eddelbuettel.com}
  - code: b
    address: \url{https://romain.rbind.io/}

# For footer text
lead_author_surname: Eddelbuettel and François

# Place DOI URL or CRAN Package URL here
doi: "https://cran.r-project.org/package=Rcpp"

# Abstract
abstract: |
  This note discusses \textsl{Rcpp modules}. \textsl{Rcpp modules} allow programmers to
  expose \proglang{C++} functions and classes to \proglang{R} with relative
  ease.  \textsl{Rcpp modules} are inspired from the \pkg{Boost.Python}
  \proglang{C++} library \citep{Abrahams+Grosse-Kunstleve:2003:Boost.Python}
  which provides similar features for \proglang{Python}.

# Optional: Acknowledgements
# acknowledgements: |

# Optional: One or more keywords
keywords:
  - Rcpp
  - modules
  - R
  - C++

# Font size of the document, values of 9pt (default), 10pt, 11pt and 12pt
fontsize: 9pt

# Optional: Force one-column layout, default is two-column
#one_column: true

# Optional: Enables lineo mode, but only if one_column mode is also true
#lineno: true

# Optional: Enable one-sided layout, default is two-sided
#one_sided: true

# Optional: Enable section numbering, default is unnumbered
numbersections: true

# Optional: Specify the depth of section number, default is 5
#secnumdepth: 5

# Optional: Bibliography
bibliography: Rcpp

# Optional: Enable a 'Draft' watermark on the document
#watermark: false

# Customize footer, eg by referencing the vignette
footer_contents: "Rcpp Vignette"

# Omit \pnasbreak at end
skip_final_break: true

# Produce a pinp document
output: pinp::pinp

header-includes: >
  \newcommand{\proglang}[1]{\textsf{#1}}
  \newcommand{\pkg}[1]{\textbf{#1}}
  \newcommand{\faq}[1]{FAQ~\ref{#1}}
  \newcommand{\rdoc}[2]{\href{http://www.rdocumentation.org/packages/#1/functions/#2}{\code{#2}}}
  \newcommand{\sugar}{\textsl{Rcpp sugar}~}
  \newcommand{\ith}{\textsl{i}-\textsuperscript{th}}

vignette: >
  %\VignetteIndexEntry{Rcpp-modules}
  %\VignetteKeywords{Rcpp, modules, R, Cpp}
  %\VignettePackage{Rcpp}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

# Motivation

Exposing \proglang{C++} functionality to \proglang{R} is greatly facilitated
by the \pkg{Rcpp} package and its underlying \proglang{C++} library
\citep{CRAN:Rcpp,JSS:Rcpp}. \pkg{Rcpp} smoothes many of the rough edges in
\proglang{R} and \proglang{C++} integration by replacing the traditional
\proglang{R} Application Programming Interface (API) described in
`\textsl{Writing R Extensions}' \citep{R:Extensions} with a consistent set of \proglang{C++}
classes. The '\textsl{Rcpp-introduction}' vignette \citep{CRAN:Rcpp,JSS:Rcpp} describes the API and
provides an introduction to using \pkg{Rcpp}.

These \pkg{Rcpp} facilities offer a lot of assistance to the programmer
wishing to interface \proglang{R} and \proglang{C++}. At the same time, these
facilities are limited as they operate on a function-by-function basis. The
programmer has to implement a `.Call` compatible function (to
conform to the \proglang{R} API) using classes of the \pkg{Rcpp} API as
described in the next section.

## Exposing functions using \pkg{Rcpp}

Exposing existing \proglang{C++} functions to \proglang{R} through \pkg{Rcpp}
usually involves several steps. One approach is to write an additional wrapper
function that is responsible for converting input objects to the appropriate
types, calling the actual worker function and converting the results back to
a suitable type that can be returned to \proglang{R} (`SEXP`).
Consider the `norm` function below:

```cpp
double norm( double x, double y ) {
    return sqrt( x*x + y*y );
}
```

This simple function does not meet the requirements set by the `.Call`
convention, so it cannot be called directly by \proglang{R}. Exposing the
function involves writing a simple wrapper function
that does match the `.Call` requirements. \pkg{Rcpp} makes this easy.

```cpp
using namespace Rcpp;
RcppExport SEXP norm_wrapper(SEXP x_, SEXP y_) {
    // step 0: convert input to C++ types
    double x = as<double>(x_), y = as<double>(y_);

    // step 1: call the underlying C++ function
    double res = norm(x, y);

    // step 2: return the result as a SEXP
    return wrap(res);
}
```

Here we use the (templated) \pkg{Rcpp} converter `as()` which can
transform from a `SEXP` to a number of different \proglang{C++} and
\pkg{Rcpp} types. The \pkg{Rcpp} function `wrap()` offers the opposite
functionality and converts many known types to a `SEXP`.

This process is simple enough, and is used by a number of CRAN packages.
However, it requires direct involvement from the programmer, which quickly
becomes tiresome when many functions are involved. \textsl{Rcpp modules}
provides a much more elegant and unintrusive way to expose \proglang{C++}
functions such as the `norm` function shown above to \proglang{R}.

We should note that \pkg{Rcpp} now has \textsl{Rcpp attributes} which extends
certain aspect of \textsl{Rcpp modules} and makes binding to simple functions
such as this one even easier.  With \textsl{Rcpp attribues} we can just write

```cpp
# include <Rcpp.h>

// [[Rcpp::export]]
double norm(double x, double y) {
    return sqrt(x*x + y*y);
}
```

See the corresponding vignette \citep{CRAN:Rcpp:Attributes} for details, but
read on for \textsl{Rcpp modules} which contains to provide features not
covered by \textsl{Rcpp attributes}, particularly when it comes to binding
entire C++ classes and more.

## Exposing classes using Rcpp

Exposing \proglang{C++} classes or structs is even more of a challenge because it
requires writing glue code for each member function that is to be exposed.

Consider the simple `Uniform` class below:

```cpp
class Uniform {
public:
    Uniform(double min_, double max_) : 
       min(min_), max(max_) {}

    NumericVector draw(int n) {
        RNGScope scope;
        return runif(n, min, max);
    }

private:
    double min, max;
};
```

To use this class from R, we at least need to expose the constructor and
the `draw` method. External pointers
\citep{R:Extensions} are the perfect vessel for this, and using the
`Rcpp:::XPtr` template from \pkg{Rcpp} we can expose the class
with these two functions:

```cpp
using namespace Rcpp;

/// create external pointer to a Uniform object
RcppExport SEXP Uniform__new(SEXP min_, 
                             SEXP max_) {
    // convert inputs to appropriate C++ types
    double min = as<double>(min_), 
           max = as<double>(max_);

    // create pointer to an Uniform object and 
    // wrap it as an external pointer
    Rcpp::XPtr<Uniform> 
    ptr( new Uniform( min, max ), true );

    // return the external pointer to the R side
    return ptr;
}

/// invoke the draw method
RcppExport SEXP Uniform__draw(SEXP xp, SEXP n_) {
    // grab the object as a XPtr (smart pointer) 
    // to Uniform
    Rcpp::XPtr<Uniform> ptr(xp);

    // convert the parameter to int
    int n = as<int>(n_);

    // invoke the function
    NumericVector res = ptr->draw( n );

    // return the result to R
    return res;
}
```

As it is generally a bad idea to expose external pointers `as is',
they usually get wrapped as a slot of an S4 class.

Using `cxxfunction()` from the \pkg{inline} package, we can build this
example on the fly. Suppose the previous example code assigned to a text variable
`unifModcode`, we could then do

<!--
DE 21 Sep 2013: there must a bug somewhere in the vignette processing
             as the following example produces only empty lines preceded
             by '+' -- same for 0.10.4 release and current 0.10.5 pre-release
             hence shortened example to not show code again
-->

```{r, eval=FALSE}
f1 <- cxxfunction( , "", includes = unifModCode,
                  plugin = "Rcpp" )
getDynLib(f1)  ## will display info about 'f1' 
```

The following listing shows some \textsl{manual} wrapping to access the code,
we will see later how this can be automated:

```{r, eval=FALSE}
setClass("Uniform",
         representation( pointer = "externalptr"))

# helper
Uniform_method <- function(name) {
    paste("Uniform", name, sep = "__")
}

# syntactic sugar to allow object$method( ... )
setMethod("$", "Uniform", function(x, name) {
    function(...)
        .Call(Uniform_method(name) ,
              x@pointer, ...)
} )
# syntactic sugar to allow new( "Uniform", ... )
setMethod("initialize", "Uniform",
          function(.Object, ...) {
    .Object@pointer <- 
        .Call(Uniform_method("new"), ...)
    .Object
} )

u <- new("Uniform", 0, 10)
u$draw( 10L )
```

\pkg{Rcpp} considerably simplifies the code that would
be involved for using external pointers with the traditional \proglang{R} API.
Yet this still involves a lot of mechanical code that quickly
becomes hard to maintain and error prone.
\textsl{Rcpp modules} offer an elegant way to expose the `Uniform`
class in a way that makes both the internal
\proglang{C++} code and the \proglang{R} code easier.



# Rcpp modules 

The design of Rcpp modules has been influenced by \proglang{Python} modules which are generated by the
`Boost.Python` library \citep{Abrahams+Grosse-Kunstleve:2003:Boost.Python}.
Rcpp modules provide a convenient and easy-to-use way
to expose \proglang{C++} functions and classes to \proglang{R}, grouped
together in a single entity.

A Rcpp module is created in a `cpp` file using the `RCPP_MODULE`
macro, which then provides declarative code of what the module
exposes to \proglang{R}.

## Exposing \proglang{C++} functions using Rcpp modules

Consider the `norm` function from the previous section.
We can expose it to \proglang{R} :

```cpp
using namespace Rcpp;

double norm(double x, double y) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod) {
    function("norm", &norm);
}
```

The code creates an Rcpp module called `mod`
that exposes the `norm` function. \pkg{Rcpp} automatically
deduces the conversions that are needed for input and output. This alleviates
the need for a wrapper function using either \pkg{Rcpp} or the \proglang{R} API.

On the \proglang{R} side, the module is retrieved by using the
`Module` function from \pkg{Rcpp}

```{r, eval=FALSE}
inc <- '
using namespace Rcpp;

double norm( double x, double y ) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod) {
    function("norm", &norm);
}
'

fx <- cxxfunction(signature(),
                  plugin="Rcpp", include=inc)
mod <- Module("mod", getDynLib(fx))
```

Note that this example assumed that the previous code segment defining the
module was returned by the `cxxfunction()` (from the \pkg{inline}
package) as callable R function `fx` from which we can extract the
relevant pointer using `getDynLib()`.  In the case of using Rcpp modules
via a package (which is detailed in Section~\ref{sec:package} below), modules
are actually loaded differently and we would have used

```{r, eval=FALSE}
require(nameOfMyModulePackage)
mod <- new( mod )
mod$norm( 3, 4 )
```

where the module is loaded upon startup and we use the constructor
directly. More details on this aspect follow below.

A module can contain any number of calls to `function` to register
many internal functions to \proglang{R}. For example, these 6 functions :

```cpp
std::string hello() {
    return "hello";
}

int bar( int x) {
    return x*2;
}

double foo( int x, double y) {
    return x * y;
}

void bla( ) {
    Rprintf("hello\\n");
}

void bla1( int x) {
    Rprintf("hello (x = %d)\\n", x);
}

void bla2( int x, double y) {
    Rprintf("hello (x = %d, y = %5.2f)\\n", x, y);
}
```

can be exposed with the following minimal code:

```cpp
RCPP_MODULE(yada) {
    using namespace Rcpp;

    function("hello" , &hello);
    function("bar"   , &bar  );
    function("foo"   , &foo  );
    function("bla"   , &bla  );
    function("bla1"  , &bla1 );
    function("bla2"  , &bla2 );
}
```

which can then be used from \proglang{R}:

```{r, eval=FALSE}
require(Rcpp)

yd <- Module("yada", getDynLib(fx))
yd$bar(2L)
yd$foo(2L, 10.0)
yd$hello()
yd$bla()
yd$bla1(2L)
yd$bla2(2L, 5.0)
```

In the case of a package (as for example the one created by
`Rcpp.package.skeleton()` with argument `module=TRUE`; more on that
below), we can use

```{r, eval=FALSE}
require(myModulePackage)    ## if another name

bar(2L)
foo(2L, 10.0)
hello()
bla()
bla1(2L)
bla2(2L, 5.0)
```


The requirements for a function to be exposed to \proglang{R} via Rcpp modules
are:

- The function takes between 0 and 65 parameters.
- The type of each input parameter must be manageable by the `Rcpp::as` template.
- The return type of the function must be either `void` or any type that
  can be managed by the `Rcpp::wrap` template.
- The function name itself has to be unique in the module.
  In other words, no two functions with
  the same name but different signatures are allowed. C++ allows overloading
  functions. This might be added in future versions of modules.

### Documentation for exposed functions using Rcpp modules

In addition to the name of the function and the function pointer, it is possible
to pass a short description of the function as the third parameter of `function`.

```cpp
using namespace Rcpp;

double norm(double x, double y) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod) {
    function("norm", &norm, 
             "Provides a simple vector norm");
}
```

The description is used when displaying the function to the R prompt:

```{r, eval=FALSE}
mod <- Module("mod", getDynLib(fx))
show(mod$norm)
```

### Formal arguments specification

`function` also gives the possibility to specify the formal arguments
of the R function that encapsulates the C++ function, by passing
a `Rcpp::List` after the function pointer.

```cpp
using namespace Rcpp;

double norm(double x, double y) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod_formals) {
    function("norm",
             &norm,
             List::create(_["x"] = 0.0, 
                          _["y"] = 0.0),
             "Provides a simple vector norm");
}
```

A simple usage example is provided below:

```{r, eval=FALSE}
norm <- mod$norm
norm()
norm(y = 2)
norm(x = 2, y = 3)
args(norm)
```

To set formal arguments without default values, simply omit the rhs.

```cpp
using namespace Rcpp;

double norm(double x, double y) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod_formals2) {
    function("norm", &norm,
             List::create(_["x"], _["y"] = 0.0),
             "Provides a simple vector norm");
}
```

This can be used as follows:

```{r, eval=FALSE}
norm <- mod$norm
args(norm)
```

The ellipsis (`...`) can be used to denote that additional arguments
are optional; it does not take a default value.

```cpp
using namespace Rcpp;

double norm(double x, double y) {
    return sqrt(x*x + y*y);
}

RCPP_MODULE(mod_formals3) {
    function("norm", &norm,
             List::create(_["x"], _["..."]),
             "documentation for norm");
}
```

and from the R side:

```{r, eval=FALSE}
norm <- mod$norm
args(norm)
```


## Exposing \proglang{C++} classes using Rcpp modules

Rcpp modules also provide a mechanism for exposing \proglang{C++} classes, based
on the reference classes introduced in R 2.12.0.

### Initial example

A class is exposed using the `class_` keyword. The `Uniform`
class may be exposed to \proglang{R} as follows:

```cpp
using namespace Rcpp;
class Uniform {
public:
    Uniform(double min_, double max_) : 
       min(min_), max(max_) {}

    NumericVector draw(int n) const {
        RNGScope scope;
        return runif(n, min, max);
    }

    double min, max;
};

double uniformRange(Uniform* w) {
    return w->max - w->min;
}

RCPP_MODULE(unif_module) {

    class_<Uniform>("Uniform")

    .constructor<double,double>()

    .field("min", &Uniform::min)
    .field("max", &Uniform::max)

    .method("draw", &Uniform::draw)
    .method("range", &uniformRange)
    ;

}
```

```{r, eval=FALSE}
## assumes   fx_unif <- cxxfunction(...)   ran
unif_module <- Module("unif_module",
                      getDynLib(fx_unif))
Uniform <- unif_module$Uniform
u <- new(Uniform, 0, 10)
u$draw(10L)
u$range()
u$max <- 1
u$range()
u$draw(10)
```

`class_` is templated by the \proglang{C++} class or struct
that is to be exposed to \proglang{R}.
The parameter of the `class_<Uniform>` constructor is the name we will
use on the \proglang{R} side. It usually makes sense to use the same name as the class
name. While this is not enforced, it might be useful when exposing a class
generated from a template.

Then constructors, fields and methods are exposed.

### Exposing constructors using Rcpp modules

Public constructors that take from 0 and 6 parameters can be exposed
to the R level using the `.constuctor` template method of `.class_`.

Optionally, `.constructor` can take a description as the first argument.

```cpp
 .constructor<double,double>("sets the min and "
      "max value of the distribution")
```

Also, the second argument can be a function pointer (called validator)
matching the following type :

```cpp
typedef bool (*ValidConstructor)(SEXP*,int);
```

The validator can be used to implement dispatch to the appropriate constructor,
when multiple constructors taking the same number of arguments are exposed.
The default validator always accepts the constructor as valid if it is passed
the appropriate number of arguments. For example, with the call above, the default
validator accepts any call from R with two `double` arguments (or
arguments that can be cast to `double`).

TODO: include validator example here

### Exposing fields and properties

`class_` has three ways to expose fields and properties, as
illustrated in the example below :

```cpp
using namespace Rcpp;
class Foo {
public:
    Foo(double x_, double y_, double z_):
        x(x_), y(y_), z(z_) {}

    double x;
    double y;

    double get_z() { return z; }
    void set_z(double z_) { z = z_; }

private:
    double z;
};

RCPP_MODULE(mod_foo) {
    class_<Foo>( "Foo" )

    .constructor<double,double,double>()

    .field("x", &Foo::x)
    .field_readonly("y", &Foo::y)

    .property("z", &Foo::get_z, &Foo::set_z)
    ;
}
```

The `.field` method exposes a public field with read/write access from R.
`field` accepts an extra parameter to give a short description of the
field:

```cpp
  .field("x", &Foo::x, "documentation for x")
```

The `.field_readonly` exposes a public field with read-only access from R.
It also accepts the description of the field.

```cpp
  .field_readonly("y", &Foo::y, 
                  "documentation for y")
```

The `.property` method allows indirect access to fields through
a getter and a setter. The setter is optional, and the property is considered
read-only if the setter is not supplied. A description of the property is also
allowed:

```cpp
  // with getter and setter
  .property("z", &Foo::get_z, 
            &Foo::set_z, "Documentation for z")

  // with only getter
  .property("z", 
            &Foo::get_z, "Documentation for z")
```

The type of the field (\textbf{T}) is deduced from the return type of the getter, and if a
setter is given its unique parameter should be of the same type.

Getters can be member functions taking no parameter and returning a \textbf{T}
(for example `get_z` above), or
a free function taking a pointer to the exposed
class and returning a \textbf{T}, for example:

```cpp
double z_get(Foo* foo) { return foo->get_z(); }
```

Setters can be either a member function taking a `T` and returning void, such
as `set_z` above, or a free function taking a pointer to the target
class and a \textbf{T} :

```cpp
void z_set(Foo* foo, double z) { foo->set_z(z); }
```

Using properties gives more flexibility in case field access has to be tracked
or has impact on other fields. For example, this class keeps track of how many times
the `x` field is read and written.

```cpp
class Bar {
public:

    Bar(double x_) : x(x_), nread(0), nwrite(0) {}

    double get_x() {
        nread++;
        return x;
    }

    void set_x(double x_) {
        nwrite++;
        x = x_;
    }

    IntegerVector stats() const {
        return 
        IntegerVector::create(_["read"] = nread,
                              _["write"] = nwrite);
    }

private:
    double x;
    int nread, nwrite;
};

RCPP_MODULE(mod_bar) {
    class_<Bar>( "Bar" )

    .constructor<double>()

    .property( "x", &Bar::get_x, &Bar::set_x )
    .method( "stats", &Bar::stats )
    ;
}
```

Here is a simple usage example:

```{r, eval=FALSE}
Bar <- mod_bar$Bar
b <- new(Bar, 10)
b$x + b$x
b$stats()
b$x <- 10
b$stats()
```

### Exposing methods using Rcpp modules

`class_` has several overloaded and templated `.method`
functions allowing the programmer to expose a method associated with the class.

A legitimate method to be exposed by `.method` can be:

- A public member function of the class, either const or non const, that
  returns void or any type that can be handled by `Rcpp::wrap`, and that
  takes between 0 and 65 parameters whose types can be handled by `Rcpp::as`.
- A free function that takes a pointer to the target class as its first
  parameter, followed by 0 or more (up to 65) parameters that can be handled by
  `Rcpp::as` and returning a type that can be handled by `Rcpp::wrap`
  or void.

### Documenting methods

`.method` can also include a short documentation of the method, after the method
(or free function) pointer.

```cpp
.method("stats", &Bar::stats,
        "vector indicating the number of "
        "times x has been read and written")
```

TODO: mention overloading, need good example.


### Const and non-const member functions

`method` is able to expose both `const` and `non const`
member functions of a class. There are however situations where
a class defines two versions of the same method, differing only in their
signature by the `const`-ness. It is for example the case of the
member functions `back` of the `std::vector` template from
the STL.

```cpp
reference back ( );
const_reference back ( ) const;
```

To resolve the ambiguity, it is possible to use `const_method`
or `nonconst_method` instead of `method` in order
to restrict the candidate methods.

### Special methods

\pkg{Rcpp} considers the methods `[[` and `[[<-` special, 
and promotes them to indexing methods on the \proglang{R} side.

### Object finalizers

The `.finalizer` member function of `class_` can be used to
register a finalizer. A finalizer is a free function that takes a pointer
to the target class and return `void`. The finalizer is called
before the destructor and so operates on a valid object of the target class.

It can be used to perform operations, releasing resources, etc ...

The finalizer is called automatically when the \proglang{R} object that encapsulates
the \proglang{C++} object is garbage collected.

### S4 dispatch

When a \proglang{C++} class is exposed by the `class_` template,
a new S4 class is registered as well. The name of the S4 class is
obfuscated in order to avoid name clashes (i.e. two modules exposing the
same class).

This allows implementation of \proglang{R}-level
(S4) dispatch. For example, one might implement the `show`
method for \proglang{C++} `World` objects:

```{r, eval=FALSE}
setMethod("show", yada$World , function(object) {
    msg <- paste("World object with message : ",
                 object$greet())
    writeLines(msg)
} )
```

TODO: mention R inheritance (John ?)

### Full example

<!-- TODO: maybe replace this by something from wls or RcppModels ? -->

The following example illustrates how to use Rcpp modules to expose
the class `std::vector<double>` from the STL.

```cpp
typedef std::vector<double> vec; 	
void vec_assign(vec* obj, 
                Rcpp::NumericVector data) {
    obj->assign(data.begin(), data.end());
}
void vec_insert(vec* obj, int position, 
                Rcpp::NumericVector data) {
    vec::iterator it = obj->begin() + position;
    obj->insert(it, data.begin(), data.end());
}
Rcpp::NumericVector vec_asR( vec* obj ) { 
   return Rcpp::wrap( *obj ); 
}
void vec_set(vec* obj, int i, double value) { 
   obj->at( i ) = value; 
}

RCPP_MODULE(mod_vec) {
    using namespace Rcpp;

    // we expose class std::vector<double> 
    // as "vec" on the R side
    class_<vec>("vec")

    // exposing constructors
    .constructor()
    .constructor<int>()

    // exposing member functions
    .method("size", &vec::size)
    .method("max_size", &vec::max_size)
    .method("resize", &vec::resize)
    .method("capacity", &vec::capacity)
    .method("empty", &vec::empty)
    .method("reserve", &vec::reserve)
    .method("push_back", &vec::push_back)
    .method("pop_back", &vec::pop_back)
    .method("clear", &vec::clear)

    // exposing const member functions
    .const_method("back", &vec::back)
    .const_method("front", &vec::front)
    .const_method("at", &vec::at )

    // exposing free functions taking a 
    // std::vector<double>* as their first 
    // argument
    .method("assign", &vec_assign)
    .method("insert", &vec_insert)
    .method("as.vector", &vec_asR)

    // special methods for indexing
    .const_method("[[", &vec::at)
    .method("[[<-", &vec_set)
    ;
}
```

```{r, eval=FALSE}
# for code compiled on the fly using
# cxxfunction() into 'fx_vec', we use
mod_vec <- Module("mod_vec",
                  getDynLib(fx_vec),
                  mustStart = TRUE)
vec <- mod_vec$vec
# and that is not needed in a package
# setup as e.g. one created
# via Rcpp.package.skeleton(..., module=TRUE)
v <- new(vec)
v$reserve(50L)
v$assign(1:10)
v$push_back(10)
v$size()
v$capacity()
v[[ 0L ]]
v$as.vector()
```

# Using modules in other packages {#sec:package}

## Namespace import/export

### Import all functions and classes

When using \pkg{Rcpp} modules in a packages, the client package needs to
import \pkg{Rcpp}'s namespace. This is achieved by adding the
following line to the `NAMESPACE` file.

```{r, echo=FALSE,eval=TRUE}
options( prompt = " ", continue = " " )
```

```{r, eval=FALSE}
import(Rcpp)
```

In some case we have found that explicitly naming a symbol can be preferable:

```{r, eval=FALSE}
import(Rcpp, evalCpp)
```

## Load the module

### Deprecated older method using loadRcppModules

Note: This approach is deprecated as of Rcpp 0.12.5, and now triggers a warning
message.  Eventually this function will be withdrawn.

The simplest way to load all functions and classes from a module directly
into a package namespace used to be to use the `loadRcppModules` function
within the `.onLoad` body.

```{r, eval=FALSE}
.onLoad <- function(libname, pkgname) {
    loadRcppModules()
}
```

This will look in the package's DESCRIPTION file for the `RcppModules`
field, load each declared module and populate their contents into the
package's namespace. For example, both the \pkg{testRcppModule} package
(which is part of large unit test suite for \pkg{Rcpp}) and the package
created via `Rcpp.package.skeleton("somename", module=TRUE)` have this
declaration:

```
RcppModules: yada, stdVector, NumEx
```

The `loadRcppModules` function has a single argument `direct`
with a default value of `TRUE`. With this default value, all content
from the module is exposed directly in the package namespace. If set to
`FALSE`, all content is exposed as components of the module.

### Preferred current method using loadModule

Starting with release 0.9.11, an alternative is provided by the
`loadModule()` function which takes the module name as an argument.
It can be placed in any `.R` file in the package. This is useful as it allows to load
the module from the same file as some auxiliary \proglang{R} functions using the
module. For the example module, the equivalent code to the `.onLoad()`
use shown above then becomes

```{r, eval=FALSE}
loadModule("yada")
loadModule("stdVector")
loadModule("NumEx")
```

This feature is also used in the new Rcpp Classes introduced with Rcpp 0.9.11.

### Just expose the module

Alternatively, it is possible to just expose the module to the user of the package,
and let them extract the functions and classes as needed. This uses lazy loading
so that the module is only loaded the first time the user attempts to extract
a function or a class with the dollar extractor.

```{r, eval=FALSE}
yada <- Module( "yada" )

.onLoad <- function(libname, pkgname) {
    # placeholder
}
```

```{r, echo=FALSE,eval=TRUE}
options(prompt = "> ", continue = "+ ")
```


## Support for modules in skeleton generator

The `Rcpp.package.skeleton` function has been improved to help
\pkg{Rcpp} modules. When the `module` argument is set to `TRUE`,
the skeleton generator installs code that uses a simple module.

```{r, eval=FALSE}
Rcpp.package.skeleton("testmod", module = TRUE)
```

Creating a new package using \textsl{Rcpp modules} is easiest via the call to
`Rcpp.package.skeleton()` with argument `module=TRUE` as a working
package with three example Modules results.

## Module documentation

\pkg{Rcpp} defines a `prompt` method for the
`Module` class, allowing generation of a skeleton of an Rd
file containing some information about the module.

```{r, eval=FALSE}
yada <- Module("yada")
prompt(yada, "yada-module.Rd")
```

We strongly recommend using a package when working with Modules.  But in case a
manually compiled shared library has to loaded, the return argument of the
`getDynLib()` function can be supplied as the `PACKAGE` argument to
the `Module()` function as well.


# Future extensions {#sec:future}

`Boost.Python` has many more features that we would like to port
to Rcpp modules : class inheritance, default arguments, enum
types, ...

# Known shortcomings {#sec:misfeatures}

There are some things \textsl{Rcpp modules} is not good at:

- serialization and deserialization of objects: modules are
  implemented via an external pointer using a memory location, which is
  non-constant and varies between session. Objects have to be re-created,
  which is different from the (de-)serialization that R offers. So these
  objects cannot be saved from session to session.
- mulitple inheritance: currently, only simple class structures are
  representable via \textsl{Rcpp modules}.

# Summary

This note introduced \textsl{Rcpp modules} and illustrated how to expose
\proglang{C++} function and classes more easily to \proglang{R}.
We hope that \proglang{R} and \proglang{C++} programmers
find \textsl{Rcpp modules} useful.


