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# pure-gsl - GNU Scientific Library Interface for Pure

Version 0.11, June 03, 2012

Albert Graef <Dr.Graef@t-online.de>
Eddie Rucker <erucker@bmc.edu>

License: GPL V3 or later, see the accompanying COPYING file

Building on Pure’s GSL-compatible matrix support, this module aims to provide a complete wrapper for the GNU Scientific Library which provides a wide range of mathematical routines useful for scientific programming, number crunching and signal processing applications.

This is still work in progress, only a small part of the interface is finished right now. Here is a brief summary of the operations which are implemented:

- Matrix-scalar and matrix-matrix arithmetic. This is fairly complete and includes matrix multiplication, as well as element-wise exponentiation (^) and integer operations (div, mod, bit shifts and bitwise logical operations) which aren’t actually in the GSL API.
- SVD (singular value decomposition), as well as the corresponding solvers, pseudo inverses and left and right matrix division. This is only available for real matrices right now, as GSL doesn’t implement complex SVD.
- Random distributions (p.d.f. and c.d.f.) and statistic functions.
- Polynomial evaluation and roots.
- Linear least-squares fitting. Multi-fitting is not available yet.

Installation instructions: Get the latest source from http://pure-lang.googlecode.com/files/pure-gsl-0.11.tar.gz. Run `make` to compile the module and `make install` (as root) to install it in the Pure library directory. This requires GNU make, and of course you need to have Pure and GSL installed. The `make install` step is only necessary for system-wide installation.

`make` tries to guess your Pure installation directory and platform-specific setup. If it gets this wrong, you can set some variables manually. In particular, `make install prefix=/usr` sets the installation prefix, and `make PIC=-fPIC` or some similar flag might be needed for compilation on 64 bit systems. Please see the Makefile for details.

The current release requires GSL 1.11 or later and Pure 0.45 or later. Older GSL versions might still work, but then some operations may be missing. The latest and greatest GSL version is always available from http://www.gnu.org/software/gsl.

After installation, you can import the entire GSL interface as follows:

    using gsl;

For convenience, the different parts of the GSL interface are also available as separate modules. E.g., if you only need the matrix operations:

    using gsl::matrix;

In either case, the global `gsl_version` variable reports the installed GSL version:

    > show gsl_version
    let gsl_version = "1.11";

(This variable used to be defined by the Pure runtime but has been moved into pure-gsl as of Pure 0.37.)

Most other operations are declared in separate namespaces which are in 1-1 correspondence with the module names. Thus, e.g., the `gsl_poly_eval` routine is named `gsl::poly::eval` in Pure and can be found in the `gsl::poly` module and namespace. The `using namespace` declaration can be used to facilitate access to the operations in a given namespace, e.g.:

    > using gsl::poly;
    > using namespace gsl::poly;
    > eval {1,2,3} 2;
    17

See the `examples` folder in the sources for some examples.

If you’d like to contribute, please mail the authors or contact us at http://groups.google.com/group/pure-lang.

- Polynomials
  - Routines
  - Examples
- Special Functions
  - Airy Functions
  - Examples
  - Bessel Functions
  - Examples
  - Clausen Functions
  - Examples
  - Colomb Functions
  - Examples
  - Coupling Coefficients
  - Examples
  - Dawson Function
  - Examples
  - Debye Functions
  - Examples
  - Dilogarithm
  - Examples
  - Examples
- Matrices
  - Matrix Creation
  - Matrix Operators and Functions
  - Singular Value Decomposition
- Least-Squares Fitting
  - Routines
  - Examples
- Statistics
  - Routines
  - Examples
- Random Number Distributions
  - Routines
  - Examples
- Sorting
  - Routines
  - Examples

## Polynomials

This module provides Pure wrappers for the GSL polynomial routines. For detail about the routines, see Chapter 6 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Polynomials.html.

Polynomials are represented by vectors (one row matrices).

### Routines

`gsl::poly::``eval` c::matrix x

  implements `gsl_poly_eval`, `gsl_poly_complex_eval`, and `gsl_complex_poly_eval` without the `len` parameter.

  GSL does not supply an integer routine for evaluating polynomials with `int` or `bigint` coefficients. Therefore, an integer routine has been provided in pure-gsl using the Chinese Remainder Theorem.

`gsl::poly::``dd_init` x::matrix y::matrix

  implements `gsl_poly_dd_init` without the `size` parameter.

`gsl::poly::``dd_eval` dd::matrix xa::matrix x::double

  implements `gsl_poly_dd_eval` without the `size` parameter.

`gsl::poly::``dd_taylor` xp::double dd::matrix xa::matrix

  implements `gsl_poly_dd_taylor` without the `size` and workspace `w` arguments.

`gsl::poly::``solve_quadratic` a b c

  implements `gsl_poly_solve_quadratic`. This function returns a list of roots instead of passing them through the parameters `x0` and `x1`.

`gsl::poly::``complex_solve_quadratic` a b c

  implements `gsl_poly_complex_solve_quadratic`. This function returns a list of roots instead of passing trhough the parameters `z0` and `z1`.

`gsl::poly::``solve_cubic` a b c

  implements `gsl_poly_solve_cubic`. This function returns a list of roots instead of passing them through the parameters `x0`, `x1`, and `x2`.

`gsl::poly::``complex_solve_cubic` a b c

  implements `gsl_poly_complex_colve_cubic`. This function returns a list of roots instead of passing them through the parameters `z0`, `z1`, and `z2`.

`gsl::poly::``complex_solve` c::matrix

  implements `gsl_poly_complex_solve` omitting the parametrs `n` and `w`. The GSL routines for creating and freeing the workspace are handled automatically.

### Examples

Usage of each library routine is illustrated below.

    > using gsl::poly;
    > using namespace gsl::poly;
    > eval {1,2,3} 2;
    17
    > eval {1.0,2.0,3.0} (-2.0);
    9.0
    > eval {1, 2, 2} (1+:1);
    3.0+:6.0
    > eval {1+:2, 2+:3, 2+:3} (1+:1);
    -6.0+:11.0
    > let dd = dd_init {1,2,3} {2,4,6};
    > dd;
    {2.0,2.0,0.0}
    > dd_eval dd {1,2,3} 2;
    4.0
    > dd_taylor 0.0 dd {1,2,3};
    {0.0,2.0,0.0}
    > solve_quadratic 2 4 1;
    [-1.70710678118655,-0.292893218813452]
    > solve_quadratic 1 4 4;
    [-2.0,-2.0]
    > solve_quadratic 0 2 1;
    [-0.5]
    > solve_quadratic 1 2 8;
    []
    > complex_solve_quadratic 0 2 1;
    [-0.5+:0.0]
    > complex_solve_quadratic 2 2 3;
    [-0.5+:-1.11803398874989,-0.5+:1.11803398874989]
    > solve_cubic 3 3 1;
    [-1.0,-1.0,-1.0]
    > solve_cubic 3 2 1;
    [-2.32471795724475]
    > complex_solve_cubic 2 2 1;
    [-1.0+:0.0,-0.5+:-0.866025403784439,-0.5+:0.866025403784439]
    > complex_solve {6,1,-7,-1,1};
    [1.0+:0.0,-1.0+:0.0,-2.0+:0.0,3.0+:0.0]

## Special Functions

This module is loaded via the command `using gsl::sf` and provides Pure wrappers for the GSL Special Functions. For details, see Chapter 7 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Special-Functions.html.

To load the library, use the Pure command `using gsl::sf`. Modes for the functions must be one of:

    GSL_PREC_DOUBLE
    GSL_PREC_SINGLE
    GSL_PREC_APPROX

Results for some of the functions are returned as a Pure list instead of the `gsl_sf_result` or `gsl_sf_result_e10` structures in C. In these cases, the resulting list is one of the following forms.

> - `[val, err]` for the `gsl_sf_result` struct and
> - `[val, err, e10]` for the `gsl_sf_result_e10` struct.

### Airy Functions

`gsl::sf::``airy_Ai` x
`gsl::sf::``airy_Ai` (x, mode::int)

  implements `gsl_sf_airy_Ai`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_e` x
`gsl::sf::``airy_Ai_e` (x, mode::int)

  implements `gsl_sf_airy_Ai_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_scaled` x
`gsl::sf::``airy_Ai_scaled` (x, mode::int)

  implements `gsl_sf_airy_Ai_scaled`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_scaled_e` x
`gsl::sf::``airy_Ai_scaled_e` (x, mode::int)

  implements `gsl_sf_airy_Ai_scaled_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi` x
`gsl::sf::``airy_Bi` (x, mode::int)

  implements `gsl_sf_airy_Bi`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_e` x
`gsl::sf::``airy_Bi_e` (x, mode::int)

  implements `gsl_sf_airy_Bi_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_scaled` x
`gsl::sf::``airy_Bi_scaled` (x, mode::int)

  implements `gsl_sf_airy_Bi_scaled`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_scaled_e` x
`gsl::sf::``airy_Bi_scaled_e` (x, mode::int)

  implements `gsl_sf_airy_Bi_scaled_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_deriv` x
`gsl::sf::``airy_Ai_deriv` (x, mode::int)

  implements `gsl_sf_airy_Ai_deriv`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_deriv_e` x
`gsl::sf::``airy_Ai_deriv_e` (x, mode::int)

  implements `gsl_sf_airy_Ai_deriv_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_deriv_scaled` x
`gsl::sf::``airy_Ai_deriv_scaled` (x, mode::int)

  implements `gsl_sf_airy_Ai_deriv_scaled`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Ai_deriv_scaled_e` x
`gsl::sf::``airy_Ai_deriv_scaled_e` (x, mode::int)

  implements `gsl_sf_airy_Ai_deriv_scaled_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_deriv` x
`gsl::sf::``airy_Bi_deriv` (x, mode::int)

  implements `gsl_sf_airy_Bi_deriv`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_deriv_e` x
`gsl::sf::``airy_Bi_deriv_e` (x, mode::int)

  implements `gsl_sf_airy_Bi_deriv_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_deriv_scaled` x
`gsl::sf::``airy_Bi_deriv_scaled` (x, mode::int)

  implements `gsl_sf_airy_Bi_deriv_scaled`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_Bi_deriv_scaled_e` x
`gsl::sf::``airy_Bi_deriv_scaled_e` (x, mode::int)

  implements `gsl_sf_airy_Bi_deriv_scaled_e`. The first form computes the function with `mode = GSL_PREC_DOUBLE`.

`gsl::sf::``airy_zero_Ai` s

  implements `gsl_sf_airy_zero_Ai`.

`gsl::sf::``airy_zero_Ai_e` s

  implements `gsl_sf_airy_zero_Ai_e`.

`gsl::sf::``airy_zero_Bi` s

  implements `gsl_sf_airy_zero_Bi`.

`gsl::sf::``airy_zero_Bi_e` s

  implements `gsl_sf_airy_zero_Bi_e`.

`gsl::sf::``airy_zero_Ai_deriv` s

  implements `gsl_sf_airy_zero_Ai_deriv`.

`gsl::sf::``airy_zero_Ai_deriv_e` s

  implements `gsl_sf_airy_zero_Ai_deriv_e`.

`gsl::sf::``airy_zero_Bi_deriv` s

  implements `gsl_sf_airy_zero_Bi_deriv`.

`gsl::sf::``airy_zero_Bi_deriv_e` s

  implements `gsl_sf_airy_zero_Bi_deriv_e`.

### Examples

The following illustrate the Airy functions.

    > using gsl::sf;
    > using namespace gsl::sf;
    > airy_Ai (-1.2); // defaults to GSL_PREC_DOUBLE
    0.52619437480212
    > airy_Ai_scaled (-1.2);
    0.52619437480212
    > airy_Ai (-1.2,GSL_PREC_APPROX);
    0.526194374771687
    > airy_Ai_scaled (-1.2, GSL_PREC_SINGLE);
    0.526194374771687
    > airy_Ai_e (-1.2);
    [0.52619437480212,1.88330586480371e-15]
    > airy_Ai_e (-1.2,GSL_PREC_APPROX);
    [0.526194374771687,1.01942940819652e-08]
    > airy_Ai_scaled_e (-1.2);
    [0.52619437480212,1.88330586480371e-15]
    > airy_Ai_scaled_e (-1.2,GSL_PREC_APPROX);
    [0.526194374771687,1.01942940819652e-08]
    > airy_Bi (-1.2);
    -0.015821370184632
    > airy_Bi_scaled (-1.2);
    -0.015821370184632
    > airy_Bi (-1.2,GSL_PREC_APPROX);
    -0.0158213701898015
    > airy_Bi_scaled (-1.2, GSL_PREC_SINGLE);
    -0.0158213701898015
    > airy_Bi_e (-1.2);
    [-0.015821370184632,1.31448899295896e-16]
    > airy_Bi_e (-1.2,GSL_PREC_APPROX);
    [-0.0158213701898015,4.10638404843775e-10]
    > airy_Bi_scaled_e (-1.2);
    [-0.015821370184632,1.31448899295896e-16]
    > airy_Bi_scaled_e (-1.2,GSL_PREC_APPROX);
    [-0.0158213701898015,4.10638404843775e-10]
    > airy_Ai_deriv (-1.2); // defaults to GSL_PREC_DOUBLE
    0.107031569272281
    > airy_Ai_deriv_scaled (-1.2);
    0.107031569272281
    > airy_Ai_deriv (-1.2,GSL_PREC_APPROX);
    0.107031569264504
    > airy_Ai_deriv_scaled (-1.2, GSL_PREC_SINGLE);
    0.107031569264504
    > airy_Ai_deriv_e (-1.2);
    [0.107031569272281,3.02919983680384e-16]
    > airy_Ai_deriv_e (-1.2,GSL_PREC_APPROX);
    [0.107031569264504,9.25921017197604e-11]
    > airy_Ai_deriv_scaled_e (-1.2);
    [0.107031569272281,3.02919983680384e-16]
    > airy_Ai_deriv_scaled_e (-1.2,GSL_PREC_APPROX);
    [0.107031569264504,9.25921017197604e-11]
    > airy_Bi_deriv (-1.2);
    0.601710157437464
    > airy_Bi_deriv_scaled (-1.2);
    0.601710157437464
    > airy_Bi_deriv (-1.2,GSL_PREC_APPROX);
    0.601710157441937
    > airy_Bi_deriv_scaled (-1.2, GSL_PREC_SINGLE);
    0.601710157441937
    > airy_Bi_deriv_e (-1.2);
    [0.601710157437464,1.7029557943563e-15]
    > airy_Bi_deriv_e (-1.2,GSL_PREC_APPROX);
    [0.601710157441937,5.20534347823991e-10]
    > airy_Bi_deriv_scaled_e (-1.2);
    [0.601710157437464,1.7029557943563e-15]
    > airy_Bi_deriv_scaled_e (-1.2,GSL_PREC_APPROX);
    [0.601710157441937,5.20534347823991e-10]
    > airy_zero_Ai 2;
    -4.08794944413097
    > airy_zero_Ai_e 3;
    [-5.52055982809555,1.22581052599448e-15]
    > airy_zero_Bi 2;
    -3.27109330283635
    > airy_zero_Bi_e 3;
    [-4.83073784166202,1.07263927554824e-15]
    > airy_zero_Ai_deriv 2;
    -3.24819758217984
    > airy_zero_Ai_deriv_e 3;
    [-4.82009921117874,1.07027702504564e-15]
    > airy_zero_Bi_deriv 2;
    -4.07315508907183
    > airy_zero_Bi_deriv_e 3;
    [-5.5123957296636,1.22399773198358e-15]

### Bessel Functions

`gsl::sf::``bessel_J0` x

  implements `gsl_sf_bessel_J0`.

`gsl::sf::``bessel_J0_e` x

  implements `gsl_sf_besselJ0_e`.

`gsl::sf::``bessel_J1` x

  implements `gsl_sf_bessel_J1`.

`gsl::sf::``bessel_J1_e` x

  implements `gsl_sf_bessel_J1_e`.

`gsl::sf::``bessel_Jn` n x

  implements `gsl_sf_bessel_Jn`.

`gsl::sf::``bessel_Jn_e` n x

  implements `gsl_sf_bessel_Jn_e`.

`gsl::sf::``bessel_Jn_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_Jn_array`.

`gsl::sf::``bessel_Y0` x

  implements `gsl_sf_bessel_Y0`.

`gsl::sf::``bessel_Y0_e` x

  implements `gsl_sf_bessel_Y0_e`.

`gsl::sf::``bessel_Y1` x

  implements `gsl_sf_bessel_Y1`.

`gsl::sf::``bessel_Y1_e` x

  implements `gsl_sf_bessel_Y1_e`.

`gsl::sf::``bessel_Yn` x

  implements `gsl_sf_bessel_Yn`.

`gsl::sf::``bessel_Yn_e` x

  implements `gsl_sf_bessel_Yn_e`.

`gsl::sf::``bessel_Yn_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_Yn_array`.

`gsl::sf::``bessel_I0` x

  implements `gsl_sf_bessel_I0`.

`gsl::sf::``bessel_I0_e` x

  implements `gsl_sf_bessel_I0_e`.

`gsl::sf::``bessel_I1` x

  implements `gsl_sf_bessel_I1`.

`gsl::sf::``bessel_I1_e` x

  implements `gsl_sf_bessel_I1_e`.

`gsl::sf::``bessel_In` n::int x

  implements `gsl_sf_bessel_In`.

`gsl::sf::``bessel_In_e` n::int x

  implements `gsl_sf_bessel_In_e`

`gsl::sf::``bessel_In_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_In_array`.

`gsl::sf::``bessel_I0_scaled` x

  implements `gsl_sf_bessel_I0_scaled`.

`gsl::sf::``bessel_I0_scaled_e` x

  implements `gsl_sf_bessel_I0_scaled_e`.

`gsl::sf::``bessel_I1_scaled` x

  implements `gsl_sf_bessel_I1_scaled`.

`gsl::sf::``bessel_I1_scaled_e` x

  implements `gsl_sf_bessel_I1_scaled_e`.

`gsl::sf::``bessel_In_scaled` n::int x

  implements `gsl_sf_bessel_In_scaled`.

`gsl::sf::``bessel_In_scaled_e` n::int x

  implements `gsl_sf_bessel_In_scaled_e`.

`gsl::sf::``bessel_In_scaled_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_In_array`.

`gsl::sf::``bessel_K0` x

  implements `gsl_sf_bessel_K0`.

`gsl::sf::``bessel_K0_e` x

  implements `gsl_sf_bessel_K0_e`.

`gsl::sf::``bessel_K1` x

  implements `gsl_sf_bessel_K1`.

`gsl::sf::``bessel_K1_e` x

  implements `gsl_sf_bessel_K1_e`.

`gsl::sf::``bessel_Kn` n::int x

  implements `gsl_sf_bessel_Kn`.

`gsl::sf::``bessel_Kn_e` n::int x

  implements `gsl_sf_bessel_Kn_e`

`gsl::sf::``bessel_Kn_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_Kn_array`.

`gsl::sf::``bessel_K0_scaled` x

  implements `gsl_sf_bessel_K0_scaled`.

`gsl::sf::``bessel_K0_scaled_e` x

  implements `gsl_sf_bessel_K0_scaled_e`.

`gsl::sf::``bessel_K1_scaled` x

  implements `gsl_sf_bessel_K1_scaled`.

`gsl::sf::``bessel_K1_scaled_e` x

  implements `gsl_sf_bessel_K1_scaled_e`.

`gsl::sf::``bessel_Kn_scaled` n::int x

  implements `gsl_sf_bessel_Kn_scaled`.

`gsl::sf::``bessel_Kn_scaled_e` n::int x

  implements `gsl_sf_bessel_Kn_scaled_e`.

`gsl::sf::``bessel_Kn_scaled_array` nmin::int nmax::int x

  implements `gsl_sf_bessel_Kn_array`.

`gsl::sf::``bessel_j0` x

  implements `gsl_sf_bessel_j0`.

`gsl::sf::``bessel_j0_e` x

  implements `gsl_sf_bessel_j0_e`.

`gsl::sf::``bessel_j1` x

  implements `gsl_sf_bessel_j1`.

`gsl::sf::``bessel_j1_e` x

  implements `gsl_sf_bessel_j1_e`.

`gsl::sf::``bessel_j2` x

  implements `gsl_sf_bessel_j2`.

`gsl::sf::``bessel_j2_e` x

  implements `gsl_sf_bessel_j2_e`.

`gsl::sf::``bessel_jl` l::int x

  implements `gsl_sf_bessel_jl`.

`gsl::sf::``bessel_jl_e` l::int x

  implements `gsl_sf_bessel_jl_e`.

`gsl::sf::``bessel_jl_array` lmax::int x

  implements `gsl_sf_bessel_jl_array`.

`gsl::sf::``bessel_jl_steed_array` lmax::int x

  implements `gsl_sf_bessel_jl_steed_array`.

`gsl::sf::``bessel_y0` x

  implements `gsl_sf_bessel_y0`.

`gsl::sf::``bessel_y0_e` x

  implements `gsl_sf_bessel_y0_e`.

`gsl::sf::``bessel_y1` x

  implements `gsl_sf_bessel_y1`.

`gsl::sf::``bessel_y1_e` x

  implements `gsl_sf_bessel_y1_e`.

`gsl::sf::``bessel_y2` x

  implements `gsl_sf_bessel_y2`.

`gsl::sf::``bessel_y2_e` x

  implements `gsl_sf_bessel_y2_e`.

`gsl::sf::``bessel_yl` l::int x

  implements `gsl_sf_bessel_yl`.

`gsl::sf::``bessel_yl_e` l::int x

  implements `gsl_sf_bessel_yl_e`.

`gsl::sf::``bessel_yl_array` lmax::int x

  implements `gsl_sf_bessel_yl_array`.

`gsl::sf::``bessel_i0_scaled` x

  implements `gsl_sf_bessel_i0_scaled`.

`gsl::sf::``bessel_i0_scaled_e` x

  implements `gsl_sf_bessel_i0_scaled_e`.

`gsl::sf::``bessel_i1_scaled` x

  implements `gsl_sf_bessel_i1_scaled`.

`gsl::sf::``bessel_i1_scaled_e` x

  implements `gsl_sf_bessel_i1_scaled_e`.

`gsl::sf::``bessel_i2_scaled` x

  implements `gsl_sf_bessel_i2_scaled`.

`gsl::sf::``bessel_i2_scaled_e` x

  implements `gsl_sf_bessel_i2_scaled_e`.

`gsl::sf::``bessel_il_scaled` l::int x

  implements `gsl_sf_bessel_il_scaled`.

`gsl::sf::``bessel_il_scaled_e` l::int x

  implements `gsl_sf_bessel_il_scaled_e`.

`gsl::sf::``bessel_il_scaled_array` lmax::int x

  implements `gsl_sf_bessel_il_scaled_array`.

`gsl::sf::``bessel_k0_scaled` x

  implements `gsl_sf_bessel_k0_scaled`.

`gsl::sf::``bessel_k0_scaled_e` x

  implements `gsl_sf_bessel_k0_scaled_e`.

`gsl::sf::``bessel_k1_scaled` x

  implements `gsl_sf_bessel_k1_scaled`.

`gsl::sf::``bessel_k1_scaled_e` x

  implements `gsl_sf_bessel_ik_scaled_e`.

`gsl::sf::``bessel_k2_scaled` x

  implements `gsl_sf_bessel_k2_scaled`.

`gsl::sf::``bessel_k2_scaled_e` x

  implements `gsl_sf_bessel_k2_scaled_e`.

`gsl::sf::``bessel_kl_scaled` l::int x

  implements `gsl_sf_bessel_kl_scaled`.

`gsl::sf::``bessel_kl_scaled_e` l::int x

  implements `gsl_sf_bessel_kl_scaled_e`.

`gsl::sf::``bessel_kl_scaled_array` lmax::int x

  implements `gsl_sf_bessel_il_scaled_array`.

`gsl::sf::``bessel_Jnu` nu x

  implements `gsl_sf_bessel_Jnu`.

`gsl::sf::``bessel_Jnu_e` nu x

  implements `gsl_sf_bessel_Jnu_e`.

`gsl::sf::``bessel_sequence_Jnu_e` nu v::matrix

  implements `gsl_sf_bessel_sequence_Jnu_e`.

`gsl::sf::``bessel_Ynu` nu x

  implements `gsl_sf_bessel_Ynu`.

`gsl::sf::``bessel_Ynu_e` nu x

  implements `gsl_sf_bessel_Ynu_e`.

`gsl::sf::``bessel_Inu` nu x

  implements `gsl_sf_bessel_Inu`.

`gsl::sf::``bessel_Inu_e` nu x

  implements `gsl_sf_bessel_Inu_e`.

`gsl::sf::``bessel_Inu_scaled` nu x

  implements `gsl_sf_bessel_Inu_scaled`.

`gsl::sf::``bessel_Inu_scaled_e` nu x

  implements `gsl_sf_bessel_Inu_scaled_e`.

`gsl::sf::``bessel_Knu` nu x

  implements `gsl_sf_bessel_Knu`.

`gsl::sf::``bessel_Knu_e` nu x

  implements `gsl_sf_bessel_Knu`.

`gsl::sf::``bessel_lnKnu` nu x

  implements `gsl_sf_bessel_lnKnu`.

`gsl::sf::``bessel_lnKnu_e` nu x

  implements `gsl_sf_bessel_lnKnu_e`.

`gsl::sf::``bessel_Knu_scaled` nu x

  implements `gsl_sf_bessel_Knu_scaled`.

`gsl::sf::``bessel_Knu_scaled_e` nu x

  implements `gsl_sf_bessel_Knu_scaled_e`.

`gsl::sf::``bessel_zero_J0` s::int

  implements `gsl_sf_bessel_zero_J0`.

`gsl::sf::``bessel_zero_J0_e` s::int

  implements `gsl_sf_bessel_zero_J0_e`.

`gsl::sf::``bessel_zero_J1` s::int

  implements `gsl_sf_bessel_zero_J1`.

`gsl::sf::``bessel_zero_J1_e` s::int

  implements `gsl_sf_bessel_zero_J1_e`.

`gsl::sf::``bessel_zero_Jnu` nu s::int

  implements `gsl_sf_bessel_zero_Jnu`.

`gsl::sf::``bessel_zero_Jnu_e` nu s::int

  implements `gsl_sf_bessel_zero_Jnu`.

### Examples

The following illustrate the Bessel functions.

    > using gsl::sf;
    > using namespace gsl::sf;
    > bessel_J0 (-1.2);
    0.671132744264363
    > bessel_J0_e 0.75;
    [0.864242275166649,7.07329111491049e-16]
    > bessel_J1 1.2;
    0.498289057567216
    > bessel_J1_e (-0.2);
    [-0.099500832639236,5.00768737808415e-17]
    > bessel_Jn 0 (-1.2);
    0.671132744264363
    > bessel_Jn_e 2 0.75;
    [0.0670739972996506,5.48959386474892e-17]
    > bessel_Jn_array 0 4 0.5;
    [0.938469807240813,0.242268457674874,0.0306040234586826,
     0.00256372999458724,0.000160736476364288]
    > bessel_Y0 0.25;
    -0.931573024930059
    > bessel_Y0_e 0.25;
    [-0.931573024930059,6.4279898430593e-16]
    > bessel_Y1 0.125;
    -5.19993611253477
    > bessel_Y1_e 4.325;
    [0.343041276811844,2.74577716760089e-16]
    > bessel_Yn 3 4.325;
    -0.0684784962694202
    > bessel_Yn_e 3 4.325;
    [-0.0684784962694202,3.37764590906247e-16]
    > bessel_Yn_array 2 4 1.35;
    [-1.07379345815726,-2.66813016175689,-10.7845628163178]
    > bessel_I0 1.35;
    1.51022709775726
    > bessel_I0_e 1.35;
    [1.51022709775726,2.37852166449918e-15]
    > bessel_I1 0.35;
    0.177693400031422
    > bessel_I1_e 0.35;
    [0.177693400031422,1.55520651386126e-16]
    > bessel_In 2 3.0;
    2.24521244092995
    > bessel_In_e 2 3.0;
    2.24521244092995,5.98244771302867e-15]
    > bessel_In_array 3 5 (-0.1);
    [-2.08463574223272e-05,2.60546902129966e-07,-2.6052519298937e-09]
    > bessel_I0_scaled 1.05;
    0.453242541279856
    > bessel_I0_scaled_e 1.05;
    [0.453242541279856,4.10118141697477e-16]
    > bessel_I1_scaled 1.05;
    0.210226017612868
    > bessel_I1_scaled_e 1.05;
    [0.210226017612868,2.12903131803686e-16]
    > bessel_In_scaled 3 1.05;
    0.00903732602788281
    > bessel_In_scaled_e 3 1.05;
    [0.00903732602788281,2.00668948743994e-17]
    > bessel_In_scaled_array 3 5 1.05;
    [0.00903732602788281,0.0011701685245855,0.000121756316755217]
    > bessel_K0 2.3;
    0.0791399330020936
    > bessel_K0_e 2.3;
    [0.0791399330020936,1.15144454318261e-16]
    > bessel_K1 2.3;
    0.0949824438453627
    > bessel_K1_e 2.3;
    [0.0949824438453627,9.85583638959967e-17]
    > bessel_Kn 2 3.4;
    0.0366633035851529
    > bessel_Kn_e 2 3.4;
    [0.0366633035851529,2.01761856558251e-16]
    > bessel_Kn_array 1 3 2.5;
    [0.0738908163477471,0.121460206278564,0.268227146393449]
    > bessel_K0_scaled 1.5;
    0.367433609054158
    > bessel_K0_scaled_e 1.5;
    [0.958210053294896,1.25816573186951e-14]
    > bessel_K1_scaled 1.5;
    1.24316587355255
    > bessel_K1_scaled_e 1.5;
    [1.24316587355255,2.32370553362606e-15]
    > bessel_Kn_scaled 4 1.5;
    35.4899165934682
    > bessel_Kn_scaled_e 4 1.5;
    [35.4899165934682,3.89252285021454e-14]
    > bessel_Kn_scaled_array 4 6 1.5;
    [35.4899165934682,197.498093175689,1352.14387109806]
    > bessel_j0 0.01;
    0.999983333416666
    > bessel_j0_e 0.01;
    [0.999983333416666,4.44081808400239e-16]
    > bessel_j1 0.2;
    0.0664003806703222
    > bessel_j1_e 0.2;
    [0.0664003806703222,2.94876925856268e-17]
    > bessel_j2 0.3;
    0.00596152486862022
    > bessel_j2_e 0.3;
    [0.00596152486862022,2.64744886840705e-18]
    > bessel_jl 4 0.3;
    8.53642426502516e-06
    > bessel_jl_e 4 0.3;
    [8.53642426502516e-06,1.02355215483598e-19]
    > bessel_jl_array 2 1.2;
    [0.776699238306022,0.34528456985779,0.0865121863384538]
    > bessel_jl_steed_array 2 1.2;
    [0.776699238306022,0.34528456985779,0.0865121863384538]
    > bessel_y0 1;
    -0.54030230586814
    > bessel_y0_e 3;
    [0.329997498866815,2.93096657048522e-16]
    > bessel_y1 3;
    0.062959163602316
    > bessel_y1_e 3.0;
    [0.062959163602316,1.04609100698801e-16]
    > bessel_yl 3 5;
    -0.0154429099129942
    > bessel_yl_e 3 5;
    [-0.0154429099129942,2.87258769784673e-17]
    > bessel_i0_scaled 3;
    0.166253541303889
    > bessel_i0_scaled_e 3;
    [0.166253541303889,7.38314037924188e-17]
    > bessel_i1_scaled 3;
    0.111661944928148
    > bessel_i1_scaled_e 3;
    [0.111661944928148,4.95878648934625e-17]
    > bessel_i2_scaled 3;
    0.0545915963757409
    > bessel_i2_scaled_e 3;
    [0.0545915963757409,2.42435388989563e-17]
    > bessel_il_scaled 3 1;
    0.0037027398773348
    > bessel_il_scaled_e 3 1;
    [0.0037027398773348,8.46838615599053e-17]
    > bessel_il_scaled_array 3 1;
    [0.432332358381693,0.135335283236613,0.0263265086718556,0.0037027398773348]
    > bessel_k0_scaled 3;
    0.523598775598299
    > bessel_k0_scaled_e 3;
    [0.523598775598299,2.32524566533909e-16]
    > bessel_k1_scaled 4;
    0.490873852123405
    > bessel_k1_scaled_e 4;
    [0.490873852123405,2.17991781125539e-16]
    > bessel_k2_scaled 4;
    0.760854470791278
    > bessel_k2_scaled_e 4;
    [0.760854470791278,3.37887260744586e-16]
    > bessel_kl_scaled 2 4;
    0.760854470791278
    > bessel_kl_scaled_e 2 4;
    [0.760854470791278,3.37887260744586e-16]
    > bessel_kl_scaled_array 2 4;
    [0.392699081698724,0.490873852123405,0.760854470791278]
    > bessel_Jnu 2 2.3;
    0.413914591732062
    > bessel_Jnu_e 2 2.3;
    [0.413914591732062,6.43352513956959e-16]
    > bessel_sequence_Jnu_e 2 {.1,.2,.3};
    [0.00124895865879992,0.00498335415278356,0.011165861949064]
    > bessel_Ynu 1 0.5;
    -1.47147239267024
    > bessel_Ynu_e 1 0.5;
    [-1.47147239267024,8.49504515830242e-15]
    > bessel_Inu 1.2 3.4;
    5.25626563437082
    > bessel_Inu_e 1.2 3.4;
    [5.25626563437082,1.00839636820646e-13]
    > bessel_Inu_scaled 1.2 3.4;
    0.175418771999042
    > bessel_Inu_scaled_e 1.2 3.4;
    [0.175418771999042,3.15501414592188e-15]
    > bessel_Knu 3 3;
    0.122170375757184
    > bessel_Knu_e 3 3;
    [0.122170375757184,4.34036365096743e-16]
    > bessel_lnKnu 3 3;
    -2.10233868587978
    > bessel_lnKnu_e 3 3;
    [-2.10233868587978,4.24157124665032e-15]
    > bessel_Knu_scaled 3 3;
    2.45385759319062
    > bessel_Knu_scaled_e 3 3;
    [2.45385759319062,7.6281217575122e-15]
    > bessel_zero_J0 3;
    8.65372791291102
    > bessel_zero_J0_e 3;
    [8.65372791291102,2.59611837387331e-14]
    > bessel_zero_J1 3;
    10.1734681350627
    > bessel_zero_J1_e 3;
    [10.1734681350627,2.03469362701254e-13]
    > bessel_zero_Jnu 1.2 3;
    10.46769
    > bessel_zero_Jnu_e 1.2 3;
    [10.4676986203553,2.09353972407105e-14]86203553

### Clausen Functions

`gsl::sf::``clausen` x

  implements `gsl_sf_clausen`.

`gsl::sf::``clausen_e` x

  implements `gsl_sf_clausen_e`.

### Examples

The following illustrate the Clausen functions.

    > using gsl::sf;
    > using namespace gsl::sf;
    > clausen 4.5;
    -0.831839220823219
    > clausen_e 4.5;
    [-0.831839220823219,8.60688668835964e-16]

### Colomb Functions

The results of the Coulomb wave functions are returned as a list whose elements are ordered corresponding to the argument order of the corresponding C functions in GSL library.

`gsl::sf::``hydrogenicR_1` Z r

  implements `gsl_sf_hydrogenicR_1`.

`gsl::sf::``hydrogenicR_1_e` Z r

  implements `gsl_sf_hydrogenicR_1_e`.

`gsl::sf::``hydrogenicR` n::int l::int Z r

  implements `gsl_sf_hydrogenicR_1`.

`gsl::sf::``hydrogenicR_e` n::int l::int Z r

  implements `gsl_sf_hydrogenicR_1_e`.

`gsl::sf::``coulomb_wave_FG_e` eta x L_F k::int

  implements `gsl_sf_coulomb_wave_FG_e`.

`gsl::sf::``coulomb_wave_F_array` L_min kmax::int eta x

  implements `gsl_sf_coulomb_wave_F_array`.

`gsl::sf::``coulomb_wave_FG_array` L_min kmax::int eta x

  implements `gsl_sf_coulomb_wave_FG_array`.

`gsl::sf::``coulomb_wave_FGp_array` L_min kmax::int eta x

  implements `gsl_sf_coulomb_wave_FGp_array`.

`gsl::sf::``coulomb_wave_sphF_array` L_min kmax::int eta x

  implements `gsl_sf_coulomb_wave_sphF_array`.

`gsl::sf::``coulomb_CL_e` L eta

  implements `gsl_sf_coulomb_wave_CL_e`.

`gsl::sf::``coulomb_CL_array` Lmin kmax eta

  implements `gsl_sf_coulomb_wave_CL_array`.

### Examples

The following illustrate the Coulomb functions.

    > using gsl::sf;
    > using namespace gsl::sf;
    > hydrogenicR_1 0.2 4;
    0.0803784086420537
    > hydrogenicR_1_e 0.2 4;
    [0.0803784086420537,2.85561471862841e-17]
    > hydrogenicR 3 1 0.25 3.2;
    0.00802954301593587
    > hydrogenicR_e 3 1 0.25 3.2;
    [0.00802954301593587,3.90138748076797e-17]
    > coulomb_wave_F_array 1 2 0.5 0.5;
    [{0.0387503306520188,0.0038612830533923,0.000274978904710252},0.0]
    > coulomb_wave_FG_array 1 2 0.5 0.5;
    [{0.0387503306520188,0.0038612830533923,0.000274978904710252},
     {4.13731494044202,25.4479852847406,257.269816591168},0.0,0.0]
    > coulomb_wave_FGp_array 1 2 0.5 0.5;
    [{0.0387503306520188,0.0038612830533923,0.000274978904710252},
     {4.13731494044202,25.4479852847406,257.269816591168},0.0,0.0]
    > coulomb_wave_sphF_array 1 2 0.5 0.5;
    [{0.0775006613040376,0.0077225661067846,0.000549957809420504},0.0]
    > coulomb_CL_e (-0.5) 3;
    [0.000143036170217949,2.92195771135514e-18]
    > coulomb_CL_array (-0.5) 4 1.5;
    [0.0159218263353144,0.0251746178646226,0.00890057150292734,
     0.00172996014234001,0.000235267570111599]

### Coupling Coefficients

`gsl::sf::``coupling_3j` m::matrix

  implements `gsl_sf_coupling_3j` except the input is a 2x3 (row by column) integer matrix instead of six integer arguments.

`gsl::sf::``coupling_3j_e` m::matrix

  implements `gsl_sf_coupling_3j_e` except the input is a 2x3 (row by column) integer matrix instead of six integer arguments.

`gsl::sf::``coupling_6j` m::matrix

  implements `gsl_sf_coupling_6j` except the input is a 2x3 (row by column) integer matrix instead of six integer arguments.

`gsl::sf::``coupling_6j_e` m::matrix

  implements `gsl_sf_coupling_6j_e` except the input is a 2x3 (row by column) integer matrix instead of six integer arguments.

`gsl::sf::``coupling_9j` m::matrix

  implements `gsl_sf_coupling_9j` except the input is a 3x3 integer matrix instead of six integer arguments.

`gsl::sf::``coupling_9j_e` m::matrix

  implements `gsl_sf_coupling_9j_e` except the input is a 3x3 integer matrix instead of six integer arguments.

### Examples

The following illustrate the coupling coefficient functions.

    > using gsl::sf;
    > using namespace gsl::sf;
    > coupling_3j {6,4,2;0,0,0};
    -0.29277002188456
    > coupling_3j_e {6,4,2;0,0,0};
    [-0.29277002188456,1.300160076865e-16]
    > coupling_6j {1,2,3;2,1,2};
    -0.166666666666667
    > coupling_6j_e {1,2,3;2,1,2};
    [-0.166666666666667,2.22044604925031e-16]
    > coupling_9j {1,2,3;2,1,2;1,1,1};
    -0.0962250448649376
    > coupling_9j_e {1,2,3;2,1,2;1,1,1};
    [-0.0962250448649376,4.84948508304183e-16]

### Dawson Function

`gsl::sf::``dawson` x

  implements `gsl_sf_dawson`.

`gsl::sf::``dawson_e` x

  implements `gsl_sf_dawson_e`.

### Examples

The following illustrate the dawson functions.

    > dawson 3;/**-
    0.178271030610558
    > dawson_e 3;
    [0.178271030610558,8.9920386788099e-16]

### Debye Functions

`gsl::sf::``debye_1` x

  implements `gsl_sf_debye_1`.

`gsl::sf::``debye_1_e` x

  implements `gsl_sf_debye_1_e`.

`gsl::sf::``debye_2` x

  implements `gsl_sf_debye_2`.

`gsl::sf::``debye_2_e` x

  implements `gsl_sf_debye_2_e`.

`gsl::sf::``debye_3` x

  implements `gsl_sf_debye_3`.

`gsl::sf::``debye_3_e` x

  implements `gsl_sf_debye_3_e`.

`gsl::sf::``debye_4` x

  implements `gsl_sf_debye_4`.

`gsl::sf::``debye_4_e` x

  implements `gsl_sf_debye_4_e`.

`gsl::sf::``debye_5` x

  implements `gsl_sf_debye_5`.

`gsl::sf::``debye_5_e` x

  implements `gsl_sf_debye_5_e`.

`gsl::sf::``debye_6` x

  implements `gsl_sf_debye_6`.

`gsl::sf::``debye_6_e` x

  implements `gsl_sf_debye_6_e`.

### Examples

The following illustrate the debye functions.

    > debye_1 0.4;
    0.904437352623294
    > debye_1_e 0.4;
    [0.904437352623294,3.84040456356756e-16]
    > debye_2 1.4;
    0.613281386045505
    > debye_2_e 1.4;
    [0.613281386045505,5.15090106564116e-16]
    > debye_3 2.4;
    0.370136882985216
    > debye_3_e 2.4;
    [0.370136882985216,6.0792125556598e-16]
    > debye_4 3.4;
    0.205914922541978
    > debye_4_e 3.4;
    [0.205914922541978,7.42872979584512e-16]
    > debye_5 4.4;
    0.107477287722471
    > debye_5_e 4.4;
    [0.107477287722471,2.38647518907499e-17]
    > debye_6 5.4;
    0.0533132925698824
    > debye_6_e 5.4;
    [0.0533132925698824,1.18379289859322e-17]

### Dilogarithm

`gsl::sf::``dilog` x

  implements `gsl_sf_dilog`.

`gsl::sf::``dilog` (r<:theta)

  implements `gsl_sf_complex_dilog_e` except that results are returned as the complex value `re+:im` and the error values are not returned.

`gsl::sf::``dilog_e` x

  implements `gsl_sf_dilog_e`.

`gsl::sf::``dilog_e` (r<:theta)

  implements `gsl_sf_complex_dilog_e` except the results are returned as the list `[re+:im, re_error, im_error]`.

### Examples

The following illustrate the dilog functions.

    > dilog 1.0;
    1.64493406684823
    > dilog (1<:2);
    -0.496658586741567+:0.727146050863279
    > dilog_e (1%3);
    [0.366213229977064,8.22687466397711e-15]
    > dilog_e (1<:3);
    [-0.817454913536463+:0.0980262093913011,3.8224192909699e-15,
     1.47247478976757e-15]

`gsl::sf::``multiply_e` x y

  implements `gsl_sf_multiply_e`.

`gsl::sf::``multiply_err_e` x dx y dy

  implements `gsl_sf_multiply_err_e`.

### Examples

The following illustrate the multiply functions.

    > multiply_e 10.0 11.0;
    [110.0,4.88498130835069e-14]
    > multiply_err_e 10.0 0.04 11.0 0.002;
    [110.0,0.460000000000049]

## Matrices

This module is loaded via the command `using gsl::matrix` and provides wrappers for many of the GSL matrix, BLAS, and linear algebra routines found in Chapters 8, 12, and 13, respectively of the GSL Reference Manual:

- Vectors and Matrices
- BLAS Support
- Linear Algebra

It also contains some general utility functions for creating various types of matrices.

### Matrix Creation

The utility functions `zeros` and `ones` create matrices with all elements zero or one, respectively, and `eye` creates identity matrices. These functions can be invoked either with a pair (n,m) denoting the desired number of rows or columns, or an integer n in which case a square n x n matrix is created. The result is always a double matrix. Analogous functions `izeros`, `czeros`, etc. are provided to create integer and complex matrices, respectively.

`gsl::matrix::``zeros` (n :: int, m :: int)

  creates an n x m double matrix with all of its entries being zero.

`gsl::matrix::``zeros` n :: int

  creates an n x n double matrix with all of its entries being zero.

`gsl::matrix::``izeros` (n :: int, m :: int)

  creates an n x m integer matrix with all of its entries being zero.

`gsl::matrix::``izeros` n :: int

  creates an n`x`n integer matrix with all of its entries being zero.

`gsl::matrix::``czeros` (n :: int, m :: int)

  creates an n x m complex matrix with all of its entries being zero.

`gsl::matrix::``czeros` n :: int

  creates an n x n complex matrix with all of its entries being zero.

`gsl::matrix::``ones` (n :: int, m :: int)

  creates an n x m double matrix with all of its entries being one.

`gsl::matrix::``ones` n :: int

  creates an n x n double matrix with all of its entries being one.

`gsl::matrix::``iones` (n :: int, m :: int)

  creates an n x m integer matrix with all of its entries being one.

`gsl::matrix::``iones` n :: int

  creates an n x n integer matrix with all of its entries being one.

`gsl::matrix::``cones` (n :: int, m :: int)

  creates an n x m complex matrix with all of its entries being one.

`gsl::matrix::``cones` n :: int

  creates an n x n complex matrix with all of its entries being one.

`gsl::matrix::``eye` (n :: int, m :: int)

  creates an n x m identity matrix with double entries.

`gsl::matrix::``eye` n :: int

  creates an n x n identity matrix with double entries.

`gsl::matrix::``ieye` (n :: int, m :: int)

  creates an n x m identity matrix with integer entries.

`gsl::matrix::``ieye` n :: int

  creates an n x n identity matrix with integer entries.

`gsl::matrix::``ceye` (n :: int, m :: int)

  creates an n x m identity matrix with complex entries.

`gsl::matrix::``ceye` n :: int

  creates an n x n identity matrix with complex entries.

### Matrix Operators and Functions

The following operations are defined for constant `a` and matrices `x` and `y`. Some operators are not defined in the GSL library but are provided here for convenience.

a `+` x
x `+` a

  returns a matrix with entries `a + x!(i,j)`.

x `+` y

  adds matrix `x` to matrix `y`.

`-` x

  returns a matrix with entries `- x!(i,j)`. Note that `neg x` is equivalent to `- x`.

a `-` x

  returns a matrix with entries `a - x!(i,j)`.

x `-` a

  returns a matrix with entries `x!(i,j) - a`.

x `-` y

  subtracts matrix `y` from matrix `x`.

a `*` x
x `*` a

  returns a matrix with entries `a * x!(i,j)`.

x `.*` y

  multiplies, element-wise, matrix `x` to matrix `y`.

x `*` y

  multiplies matrix `x` to matrix `y`.

a `/` x

  returns a matrix with entries `a / x!(i,j)`. Note that matrix `x` must not have any zero entries.

x `/` a

  returns a matrix with entries `x!(i,j) / a`. Note that `a` must be nonzero.

x `./` y

  divides, element-wise, matrix x by matrix y.

x `/` y

  right divides matrix x by matrix y.

x `\` y

  left divides matrix x by matrix y.

a `div` x

  returns an integer matrix with entries `a div x!(i,j)`. Note that a must be an integer and matrix `x` must be an integer matrix with nonzero entries.

x `div` a

  returns an integer matrix with entries `x!(i,j) div a`. Note that `a` must be a nonzero integer and matrix `x` must have integer entries.

x `div` y

  computes the quotient integer matrix `x` by integer matrix `y`.

a `mod` x

  returns an integer matrix with entries `a mod x!(i,j)`. Note that `a` must be an integer and matrix `x` must be an integer matrix with nonzero entries.

x `mod` a

  returns an integer matrix with entries `a mod x!(i,j)`. Note that `a` must be an integer and matrix `x` must be an integer matrix with nonzero entries.

x `mod` y

  returns the remainder integer matrix `x` mod integer matrix `y`.

`not` x

  returns a matrix with integer entries `not x!(i,j)`. Note that `x` must be a matrix with integer entries and `not` is the bitwise negation operation.

a `^` x

  returns a matrix with entries `a ^ x!(i,j)`. Note that `0^0` is defined as 1.

x `^` a

  returns a matrix with entries `x!(i,j) ^ a`. Note that `0^0` is defined as 1.

x `.^` y

  returns a matrix with entries `x!(i,j) ^ y!(i,j)`.

x `^` y

  returns a matrix with entries `x!(i,j) ^ y!(i,j)`.

x `<<` a

  returns an integer matrix with entries `x!(i,j) << a`. Note that `a` must be an integer and matrix `x` must have integer entries.

x `<<` y

  returns an integer matrix with entries `x!(i,j) << y!(i,j)`. Note that `x` and `y` must have integer entries.

x `>>` a

  returns an integer matrix with entries `x!(i,j) >> a`. Note that `a` must be an integer and matrix `x` must have integer entries.

x `>>` y

  returns an integer matrix with entries `x!(i,j) >> y!(i,j)`. Note that `x` and `y` must have integer entries.

x `and` a
a `and` x

  returns an integer matrix with entries `a and x!(i,j)`. Note that `a` must be an integer, matrix `x` must have integer entries, and `and` is a bitwise operator.

x `and` y

  returns an integer matrix with entries `x!(i,j) and y!(i,j)`. Note that `x` and `y` must be matrices with integer entries.

x `or` a
a `or` x

  returns an integer matrix with entries `a or x!(i,j)`. Note that `a` must be an integer, matrix `x` must have integer entries, and `or` is a bitwise operator.

x `or` y

  returns an integer matrix with entries `x!(i,j) or y!(i,j)`. Note that `x` and `y` must be matrices with integer entries.

The `pow` function computes powers of matrices by repeated matrix multiplication.

`pow` x :: matrix k :: int
`pow` x :: matrix k :: bigint

  Raises matrix `x` to the `k` th power. Note `x` must be a square matrix and `k` a nonnegative integer.

### Singular Value Decomposition

For a given n x m matrix `x`, these functions yield a singular-value decomposition `u`, `s`, `v` of the matrix such that `x == u*s*transpose v`, where `u` and `v` are orthogonal matrices of dimensions n x m and n x n, respectively, and s is a n x n diagonal matrix which has the singular values in its diagonal, in descending order. Note that GSL implements this only for double matrices right now. Also, GSL only handles the case of square or overdetermined systems, but we work around that in our wrapper functions by just adding a suitable number of zero rows in the underdetermined case.

`gsl::matrix::``svd` x

  singular-value decomposition of matrix `x`.

`gsl::matrix::``svd_mod` x

  This uses the modified Golub-Reinsch algorithm, which is faster if `n > m` but needs O(m^2) extra memory as internal workspace.

`gsl::matrix::``svd_jacobi` x

  This uses one-sided Jacobi orthogonalization which provides better relative accuracy but is slower.

`gsl::matrix::``svd_solve` (u, s, v) b

  Solve the system `Ax=b`, using the SVD of `A`. `svd_solve` takes the result `(u,s,v)` of a `svd` call, and a column vector `b` of the appropriate dimension. The result is another column vector solving the system (possibly in the least-squares sense).

`gsl::matrix::``pinv` x

  Computes the pseudo inverse of a matrix from its singular value decomposition.

## Least-Squares Fitting

This module is loaded via the command `using gsl::fit` and provides Pure wrappers for the GSL least-squares fitting routines found in Chapter 36 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Least_002dSquares-Fitting.html.

### Routines

`gsl::fit::``linear` x::matrix y::matrix

  implements `gsl_fit_linear` without the `xstride`, `ystride`, and `n` parameters. Results are returned as a list `[c0, c1, cov00, cov01, cov11, sumsq]`.

`gsl::fit::``wlinear` x::matrix w::matrix y::matrix

  implements `gsl_fit_wlinear` without the `xstride`, `wstride`, `ystride`, and `n` parameters. Results are given as a list `[c0, c1, cov00, cov01, cov11, chisq]`.

`gsl::fit::``linear_est` x c0::double c1::double cov00::double cov01::double cov11::double

  implements `gsl_fit_linear_est`. Results are returned as a list `[y, y_err]`.

`gsl::fit::``mul` x::matrix y::matrix

  implements `gsl_fit_mul` omitting the parameters `xstride`, `ystride`, and `n`. Results are returned as a list `[c1, cov11, sumsq]`.

`gsl::fit::``wmul` x::matrix w::matrix y::matrix

  implements `gsl_fit_wmul` omitting the parametrs `xstride`, `ystride`, and `n`. Results are returned as a list `[c1, cov11, sumsq]`.

`gsl::fit::``mul_est` x c1::double cov11::double

  implements `gsl_fit_mul_est`. Results are returned as a list `[y, y_err]`.

### Examples

Usage of each implemented library routine is illustrated below.

    > using gsl::fit;
    > using namespace gsl::fit;

The following code determines the equation for the least-squares line through the points (1,0.01), (2,1.11), (3,1.9), (4,2.85), and (5,4.01).

    > Y x = '(a + b * x)
    > when
    >   a:b:_ = linear {1,2,3,4,5} {0.01,1.11,1.9,2.85,4.01}
    > end;
    > Y x;
    -0.946+0.974*x
    > eval $ Y 2;
    1.002

The following code illustrates estimating y-values without constructing an equation for the least-squares line determined by the points `{x1,x2,x3,...,xn}`, `{y1,y2,y3,...,yn}`. Here we estimate the y-value at x = 1, x = 2, and x = 3. Compare the output above at x = 2 to the output at x = 2 below.

    > let c0:c1:cov00:cov01:cov11:_ = linear {1,2,3,4,5}
    >   {0.01,1.11,1.9,2.85,4.01};
    > linear_est 1 c0 c1 cov00 cov01 cov11;
    [0.028,0.0838570211729465]
    > linear_est 2 c0 c1 cov00 cov01 cov11;
    [1.002,0.0592958683214944]
    > linear_est 3 c0 c1 cov00 cov01 cov11;
    [1.976,0.0484148737476408]

Next, we determine a least-squares line through the points (1,0.01), (2,1.11), (3,1.9), (4,2.85), and (5,4.01) using weights 0.1, 0.2, 0.3, 0.4, and 0.5.

    > W x = '(a + b * x)
    > when
    >   a:b:_ = wlinear (matrix (1..5))
    >           {0.1, 0.2, 0.3, 0.4, 0.5}
    >           {0.01, 1.11, 1.9, 2.85, 4.01};
    > end;
    > W u;
    -0.99+0.986*u
    > eval $ W 2;
    0.982

The least-squares slope for `Y = c1 * X` using the points (1,3), (2,5), and (3,7) is calculated below. Also, the y-values and standard error about x = 1, 2, and 3 are given.

    > let c1:cov11:sumsq:_ = mul {1,2,3} {3,5,7};
    > mul_est 1 c1 cov11;
    [2.42857142857143,0.123717914826348]
    > mul_est 2 c1 cov11;
    [4.85714285714286,0.247435829652697]
    > mul_est 3 c1 cov11;
    [7.28571428571428,0.371153744479045]

The least-squares slope for `Y = c1 * X` using the points (1,3), (2,5), and (3,7), and weights 0.4, 0.9, and 0.4 is calculated below. The approximation of y-values and standard error about x = 1, 2, and 3 follows.

    > let c1:cov11:sumsq:_ = wmul {1,2,3} {0.4,0.9,0.4} {3,5,7};
    > mul_est 1 c1 cov11;
    [2.44736842105263,0.362738125055006]
    > mul_est 2 c1 cov11;
    [4.89473684210526,0.725476250110012]
    > mul_est 3 c1 cov11;
    [7.34210526315789,1.08821437516502]

## Statistics

This module is loaded via the command `using gsl::stats` and provides Pure wrappers for the GSL Statistics routines found in Chapter 20 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Statistics.html.

### Routines

`gsl::stats::``mean` data::matrix

  implements `gsl_stats_mean` without `stride` and `n` arguments.

`gsl::stats::``variance` data::matrix

  implements `gsl_stats_variance` without `stride` and `n` arguments.

`gsl::stats::``variance` data::matrix mean

  implements `gsl_stats_variance_m` without `stride` and `n` arguments.

`gsl::stats::``sd` data::matrix

  implements `gsl_stats_sd` without `stride` and `n` arguments.

`gsl::stats::``sd_m` data::matrix mean

  implements `gsl_stats_sd_m` without `stride` and `n` arguments.

`gsl::stats::``tss` data::matrix

  implements `gsl_stats_tss` without `stride` and `n` arguments.

`gsl::stats::``tss_m` data::matrix mean

  implements `gsl_stats_tss_m` without `stride` and `n` arguments.

`gsl::stats::``variance_with_fixed_mean` data::matrix mean

  implements `gsl_stats_variance_with_fixed_mean` without `stride` and `n` arguments.

`gsl::stats::``sd_with_fixed_mean` data::matrix mean

  implements `gsl_stats_sd_with_fixed_mean` without `stride` and `n` arguments.

`gsl::stats::``absdev` data::matrix

  implements `gsl_stats_absdev` without `stride` and `n` arguments.

`gsl::stats::``absdev_m` data::matrix mean

  implements `gsl_stats_absdev_m` without `stride` and `n` arguments.

`gsl::stats::``skew` data::matrix mean

  implements `gsl_stats_skew` without `stride` and `n` arguments.

`gsl::stats::``skew_m_sd` data::matrix mean sd

  implements `gsl_stats_skew_m_sd` without `stride` and `n` arguments.

`gsl::stats::``kurtosis` data::matrix

  implements `gsl_stats_kurtosis` without `stride` and `n` arguments.

`gsl::stats::``kurtosis_m_sd` data::matrix mean sd

  implements `gsl_stats_kurtosis_m_sd` without `stride` and `n` arguments.

`gsl::stats::``lag1_autocorrelation` data::matrix

  implements `gsl_stats_lag1_autocorrelation` without `stride` and `n` arguments.

`gsl::stats::``lag1_autocorrelation_m` data::matrix mean

  implements `gsl_stats_lag1_autocorrelation_m` without `stride` and `n` arguments.

`gsl::stats::``covariance` d1::matrix d2::matrix

  implements `gsl_stats_covariance` without `stride1`, `stride2`, and `n` arguments.

`gsl::stats::``covariance_m` d1::matrix d2::matrix mean1 mean2

  implements `gsl_stats_covariance_m` without `stride1`, `stride2`, and `n` arguments.

`gsl::stats::``correlation` d1::matrix d2::matrix

  implements `gsl_stats_correlation` without `stride1`, `stride2`, and `n` arguments.

`gsl::stats::``wmean` weight::matrix data::matrix

  implements `gsl_stats_wmean` without `stride` and `n` arguments.

`gsl::stats::``wvariance` weight::matrix data::matrix

  implements `gsl_stats_wvariance` without `stride` and `n` arguments.

`gsl::stats::``wvariance_m` weight::matrix data::matrix mean

  implements `gsl_stats_wvariance_m` without `stride` and `n` arguments.

`gsl::stats::``wsd` weight::matrix data::matrix

  implements `gsl_stats_wsd` without `stride` and `n` arguments.

`gsl::stats::``wsd_m` weight::matrix data::matrix mean

  implements `gsl_stats_wsd_m` without `stride` and `n` arguments.

`gsl::stats::``wvariance_with_fixed_mean` weight::matrix data::matrix mean

  implements `gsl_stats_wvariance_with_fixed_mean` without `stride` and `n` arguments.

`gsl::stats::``wsd_with_fixed_mean` weight::matrix data::matrix mean

  implements `gsl_stats_wsd_with_fixed_mean` without `stride` and `n` arguments.

`gsl::stats::``wtss` weight::matrix data::matrix

  implements `gsl_stats_wtss` without `stride` and `n` arguments.

`gsl::stats::``wtss_m` weight::matrix data::matrix mean

  implements `gsl_stats_wtss_m` without `stride` and `n` arguments.

`gsl::stats::``wabsdev` weight::matrix data::matrix

  implements `gsl_stats_wabsdev` without `stride` and `n` arguments.

`gsl::stats::``wabsdev_m` weight::matrix data::matrix mean

  implements `gsl_stats_wabsdev_m` without `stride` and `n` arguments.

`gsl::stats::``wskew` weight::matrix data::matrix

  implements `gsl_stats_wskew` without `stride` and `n` arguments.

`gsl::stats::``wskew_m_sd` weight::matrix data::matrix mean sd

  implements `gsl_stats_wskew_m_sd` without `stride` and `n` arguments.

`gsl::stats::``wkurtosis` weight::matrix data::matrix

  implements `gsl_stats_wkurtosis` without `stride` and `n` arguments.

`gsl::stats::``wkurtosis_m_sd` weight::matrix data::matrix

  implements `gsl_stats_wkurtosis_m_sd` without `stride` and `n` arguments.

`gsl::stats::``max` data::matrix

  implements `gsl_stats_max` without `stride` and `n` arguments.

`gsl::stats::``min` data::matrix

  implements `gsl_stats_min` without `stride` and `n` arguments.

`gsl::stats::``minmax` data::matrix

  implements `gsl_stats_minmax` without `stride` and `n` arguments. Results are returned as a list `[min, max]`.

`gsl::stats::``min_index` data::matrix

  implements `gsl_stats_min_index` without `stride` and `n` arguments.

`gsl::stats::``max_index` data::matrix

  implements `gsl_stats_max_index` without `stride` and `n` arguments.

`gsl::stats::``minmax_index` data::matrix

  implements `gsl_stats_minmax_index` without `stride` and `n` arguments. Results are returned as a list `[min_index, max_index]`.

`gsl::stats::``median_from_sorted_data` data::matrix

  implements `gsl_stats_median_from_sorted_data` without `stride` and `n` arguments.

`gsl::stats::``quantile_from_sorted_data` data::matrix f::double

  implements `gsl_stats_quantile_from_sorted_data` without `stride` and `n` arguments.

### Examples

The following illustrates the use of each function in the `stats` module.

    > using gsl::stats;
    > using namespace gsl::stats;
    > mean {1,2,3,4,5};
    3.0
    > variance {1,2,3,4,5};
    2.5
    > variance_m {1,2,3,4,5}  4;
    3.75
    > sd {1,2,3,4,5};
    1.58113883008419
    > sd_m {1,2,3,4,5} 4;
    1.93649167310371
    > tss {1,2,3,4,5};
    10.0
    > tss_m {1,2,3,4,5} 4;
    15.0
    > variance_with_fixed_mean {0.0,1.2,3.4,5.6,6.0} 4.1;
    6.314
    > sd_with_fixed_mean {0.0,1.2,3.4,5.6,6.0} 4.1;
    2.51276739870606
    > absdev {2,2,3,4,4};
    0.8
    > absdev_m {2,2,3,4,4} 4;
    1.0
    > skew {1,1,1,1,2,2,2,2,2,2,2,2,3,30};
    2.94796699504537
    > skew_m_sd {1,2,2,3,3,3,3,3,3,3,4,4,5} 3 1;
    0.0
    > kurtosis  {1,2,2,3,3,3,3,3,3,3,4,4,5};
    -0.230769230769231
    > kurtosis_m_sd {1,2,2,3,3,3,3,3,3,3,4,4,5} 3 1;
    -0.230769230769231
    > lag1_autocorrelation {1,2,3,4,5};
    0.4
    > lag1_autocorrelation_m {1,2,3,4,5} 2.5;
    0.444444444444444
    > covariance {1,2,3,4,5} {3.0,4.5,6.0,7.5,9.0};
    3.75
    > covariance_m {1,2,3,4,5} {3.0,4.5,6.0,7.5,9.0} 3 6;
    3.75
    > correlation {1,2,3,4} {2,3,4,5};
    1.0
    > wmean {0.4,0.2,0.3,0.3,0.3} {2,3,4,5,6};
    3.93333333333333
    > wvariance {0.4,0.2,0.3,0.3,0.3} {2,3,4,5,6};
    2.7752808988764
    > wvariance_m {0.4,0.2,0.3,0.3,0.3} {2,3,4,5,6} 3.0;
    3.87640449438202
    > wsd {0.4,0.2,0.3,0.3,0.3} {2,3,4,5,6};
    1.66591743459164
    > wsd_m {0.4,0.2,0.3,0.3,0.3} {2,3,4,5,6} 3.0;
    1.96885867811329
    > wvariance_with_fixed_mean {1,2,3,4} {1,2,3,4} 2.5;
    1.25
    > wsd_with_fixed_mean {1,2,3,4} {1,2,3,4} 2.5;
    1.11803398874989
    > wtss {1,1,2,2} {2,3,4,5};
    6.83333333333333
    > wtss_m {1,1,2,2} {2,3,4,5} 3.1;
    10.06
    > wabsdev {1,1,2,2} {2,3,4,5};
    0.888888888888889
    > wabsdev_m {1,1,2,2} {2,3,4,5} 3.1;
    1.13333333333333
    > wskew {1,1,2,2} {2,3,4,5};
    -0.299254338484713
    > wskew_m_sd {1,1,2,2} {2,3,4,5} 3.1 1.2;
    1.33526234567901
    > wkurtosis {1,1,2,2} {2,3,4,5};
    -1.96206512878137
    > wkurtosis_m_sd {1,1,2,2} {2,3,4,5} 3.1 1.2;
    -0.681921939300412
    > min {9,4,2,1,9};
    1
    > max {9.1,4.2,2.6,1.1,9.2};
    9.2
    > minmax {9.0,4.0,2.0,1.0,9.0};
    [1.0,9.0]
    > min_index {9.1,4.2,2.6,1.1,9.2};
    3
    > max_index {9,4,2,1,9};
    0
    > minmax_index {9,4,2,1,0,9};
    [4,0]
    > median_from_sorted_data {1.0,2.0,3.0};
    2.0
    > quantile_from_sorted_data {1.0,2.0,3.0} 0.25;
    1.5

## Random Number Distributions

This module is loaded via the command `using gsl::randist` and provides Pure wrappers for the GSL random distribution routines found in Chapter 19 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Random-Number-Distributions.html.

There are two namespaces provided by randist.pure, `gsl::ran` for probability densitity functions and `gsl::cdf` for cumulative distribution functions. The two namespaces minimize typing of the prefixes `gsl_ran_` and `gsl_cdf_` respectively.

### Routines

`gsl::ran::``ugaussian_pdf` x

  implements `gsl_ran_ugaussian`.

`gsl::ran::``gaussian_pdf` x sigma

  implements `gsl_ran_gaussian_pdf`.

`gsl::ran::``gaussian_tail_pdf` x a sigma

  implements `gsl_ran_gaussian_tail_pdf`.

`gsl::ran::``ugaussian_tail_pdf` x a

  implements `gsl_ran_ugaussian_tail_pdf`.

`gsl::ran::``bivariate_gaussian_pdf` x a

  implements `gsl_ran_bivariate_gaussian_pdf`.

`gsl::ran::``exponential_pdf` x mu

  implements `gsl_ran_exponential_pdf`.

`gsl::ran::``laplace_pdf` x a

  implements `gsl_ran_laplace_pdf`.

`gsl::ran::``exppow_pdf` x a b

  implements `gsl_ran_exppow_pdf`.

`gsl::ran::``cauchy_pdf` x a

  implements `gsl_ran_cauchy_pdf`.

`gsl::ran::``rayleigh_pdf` x sigma

  implements `gsl_ran_rayleigh_pdf`.

`gsl::ran::``rayleigh_tail_pdf` x a sigma

  implements `gsl_ran_rayleigh_tail_pdf`.

`gsl::ran::``landau_pdf` x

  implements `gsl_ran_landau_pdf`.

`gsl::ran::``gamma_pdf` x a b

  implements `gsl_ran_gamma_pdf`.

`gsl::ran::``flat_pdf` x a b

  implements `gsl_ran_flat_pdf`.

`gsl::ran::``lognormal_pdf` x zeta sigma

  implements `gsl_ran_lognormal_pdf`.

`gsl::ran::``chisq_pdf` x nu

  implements `gsl_ran_chisq_pdf`.

`gsl::ran::``fdist_pdf` x nu1 nu2

  implements `gsl_ran_fdist_pdf`.

`gsl::ran::``tdist_pdf` x nu

  implements `gsl_ran_tdist_pdf`.

`gsl::ran::``beta_pdf` x a b

  implements `gsl_ran_beta_pdf`.

`gsl::ran::``logistic_pdf` x a

  implements `gsl_ran_logistic_pdf`.

`gsl::ran::``pareto_pdf` x a b

  implements `gsl_ran_pareto_pdf`.

`gsl::ran::``weibull_pdf` x a b

  implements `gsl_ran_weibull_pdf`.

`gsl::ran::``gumbel1_pdf` x a b

  implements `gsl_ran_gumbel1_pdf`.

`gsl::ran::``gumbel2_pdf` x a b

  implements `gsl_ran_gumbel2_pdf`.

`gsl::ran::``dirichlet_pdf` alpha::matrix theta::matrix

  implements `gsl_ran_dirichlet_pdf`.

`gsl::ran::``dirichlet_lnpdf` alpha::matrix theta::matrix

  implements `gsl_ran_dirichlet_lnpdf`.

`gsl::ran::``discrete_preproc` p::matrix

  implements `gsl_ran_discrete_preproc` without the `K` parameter.

`gsl::ran::``discrete_pdf` k::int p::pointer

  implements `gsl_ran_discrete_pdf` without the `K` parameter.

`gsl::ran::``discrete_free` p::pointer

  implements `gsl_ran_discrete_free`

`gsl::ran::``poisson_pdf` k::int mu

  implements `gsl_ran_poisson_pdf`.

`gsl::ran::``bernoulli_pdf` k::int p

  implements `gsl_ran_bernoulli_pdf`.

`gsl::ran::``binomial_pdf` k::int p n::int

  implements `gsl_ran_binomial_pdf`.

`gsl::ran::``multinomial_pdf` p::matrix n::matrix

  implements `gsl_ran_multinomial_pdf`.

`gsl::ran::``multinomial_lnpdf` p::matrix n::matrix

  implements `gsl_ran_multinomial_lnpdf`.

`gsl::ran::``negative_binomial_pdf` k::int p n

  implements `gsl_ran_negative_binomial_pdf`.

`gsl::ran::``pascal_pdf` k::int p n::int

  implements `gsl_ran_pascal_pdf`.

`gsl::ran::``geometric_pdf` k::int p

  implements `gsl_ran_geometric_pdf`.

`gsl::ran::``hypergeometric_pdf` k::int n1::int n2::int t::int

  implements `gsl_ran_hypergeometric_pdf`.

`gsl::ran::``logarithmic_pdf` k::int p

  implements `gsl_ran_logarithmic_pdf`.

`gsl::cdf::``ugaussian_P` x

  implements `gsl_cdf_ugaussian_P`.

`gsl::cdf::``ugaussian_Q` x

  implements `gsl_cdf_ugaussian_Q`.

`gsl::cdf::``ugaussian_Pinv` p

  implements `gsl_cdf_ugaussian_Pinv`.

`gsl::cdf::``ugaussian_Qinv` q

  implements `gsl_cdf_ugaussian_Qinv`.

`gsl::cdf::``gaussian_P` x sigma

  implements `gsl_cdf_gaussian_P`.

`gsl::cdf::``gaussian_Q` x sigma

  implements `gsl_cdf_gaussian_Q`.

`gsl::cdf::``gaussian_Pinv` p sigma

  implements `gsl_cdf_gaussian_Pinv`.

`gsl::cdf::``guassian_Qinv` q sigma

  implements `gsl_cdf_gaussian_Qinv`.

`gsl::cdf::``exponential_P` x mu

  implements `gsl_cdf_exponential_P`.

`gsl::cdf::``exponential_Q` x mu

  implements `gsl_cdf_exponential_Q`.

`gsl::cdf::``exponential_Pinv` p mu

  implements `gsl_cdf_exponential_Pinv`.

`gsl::cdf::``exponential_Qinv` q mu

  implements `gsl_cdf_exponential_Qinv`.

`gsl::cdf::``laplace_P` x a

  implements `gsl_cdf_laplace_P`.

`gsl::cdf::``laplace_Q` x a

  implements `gsl_cdf_laplace_Q`.

`gsl::cdf::``laplace_Pinv` p a

  implements `gsl_cdf_laplace_Pinv`.

`gsl::cdf::``laplace_Qinv` q a

  implements `gsl_cdf_laplace_Qinv`.

`gsl::cdf::``exppow_P` x a b

  implements `gsl_cdf_exppow_P`.

`gsl::cdf::``exppow_Q` x a b

  implements `gsl_cdf_exppow_Q`.

`gsl::cdf::``cauchy_P` x a

  implements `gsl_cdf_cauchy_P`.

`gsl::cdf::``cauchy_Q` x a

  implements `gsl_cdf_cauchy_Q`.

`gsl::cdf::``cauchy_Pinv` p a

  implements `gsl_cdf_cauchy_Pinv`.

`gsl::cdf::``cauchy_Qinv` q a

  implements `gsl_cdf_cauchy_Qinv`.

`gsl::cdf::``rayleigh_P` x sigma

  implements `gsl_cdf_rayleigh_P`.

`gsl::cdf::``rayleigh_Q` x sigma

  implements `gsl_cdf_rayleigh_Q`.

`gsl::cdf::``rayleigh_Pinv` p sigma

  implements `gsl_cdf_rayleigh_Pinv`.

`gsl::cdf::``rayleigh_Qinv` q sigma

  implements `gsl_cdf_rayleigh_Qinv`.

`gsl::cdf::``gamma_P` x a b

  implements `gsl_cdf_gamma_P`.

`gsl::cdf::``gamma_Q` x a b

  implements `gsl_cdf_gamMa_Q`.

`gsl::cdf::``gamma_Pinv` p a b

  implements `gsl_cdf_gamma_Pinv`.

`gsl::cdf::``gamma_Qinv` q a b

  implements `gsl_cdf_gamma_Qinv`.

`gsl::cdf::``flat_P` x a b

  implements `gsl_cdf_flat_P`.

`gsl::cdf::``flat_Q` x a b

  implements `gsl_cdf_flat_Q`.

`gsl::cdf::``flat_Pinv` p a b

  implements `gsl_cdf_flat_Pinv`.

`gsl::cdf::``flat_Qinv` q a b

  implements `gsl_cdf_flat_Qinv`.

`gsl::cdf::``lognormal_P` x zeta sigma

  implements `gsl_cdf_lognormal_P`.

`gsl::cdf::``lognormal_Q` x zeta sigma

  implements `gsl_cdf_lognormal_Q`.

`gsl::cdf::``lognormal_Pinv` p zeta sigma

  implements `gsl_cdf_lognormal_Pinv`.

`gsl::cdf::``lognormal_Qinv` q zeta sigma

  implements `gsl_cdf_lognormal_Qinv`.

`gsl::cdf::``chisq_P` x nu

  implements `gsl_cdf_chisq_P`.

`gsl::cdf::``chisq_Q` x nu

  implements `gsl_cdf_chisq_Q`.

`gsl::cdf::``chisq_Pinv` p nu

  implements `gsl_cdf_chisq_Pinv`.

`gsl::cdf::``chisq_Qinv` q nu

  implements `gsl_cdf_chisq_Qinv`.

`gsl::cdf::``fdist_P` x nu1 nu2

  implements `gsl_cdf_fdist_P`.

`gsl::cdf::``fdist_Q` x nu1 nu2

  implements `gsl_cdf_fdist_Q`.

`gsl::cdf::``fdist_Pinv` p nu1 nu2

  implements `gsl_cdf_fdist_Pinv`.

`gsl::cdf::``fdist_Qinv` q nu1 nu2

  implements `gsl_cdf_fdist_Qinv`.

`gsl::cdf::``tdist_P` x nu

  implements `gsl_cdf_tdist_P`.

`gsl::cdf::``tdist_Q` x nu

  implements `gsl_cdf_tdist_Q`.

`gsl::cdf::``tdist_Pinv` p nu

  implements `gsl_cdf_tdist_Pinv`.

`gsl::cdf::``tdist_Qinv` q nu

  implements `gsl_cdf_tdist_Qinv`.

`gsl::cdf::``beta_P` x a b

  implements `gsl_cdf_beta_P`.

`gsl::cdf::``beta_Q` x a b

  implements `gsl_cdf_beta_Q`.

`gsl::cdf::``beta_Pinv` p a b

  implements `gsl_cdf_beta_Pinv`.

`gsl::cdf::``beta_Qinv` q a b

  implements `gsl_cdf_beta_Qinv`.

`gsl::cdf::``logistic_P` x a

  implements `gsl_cdf_logistic_P`.

`gsl::cdf::``logistic_Q` x a

  implements `gsl_cdf_logistic_Q`.

`gsl::cdf::``logistic_Pinv` p a

  implements `gsl_cdf_logistic_Pinv`.

`gsl::cdf::``logistic_Qinv` q a

  implements `gsl_cdf_logistic_Qinv`.

`gsl::cdf::``pareto_P` x a b

  implements `gsl_cdf_pareto_P`.

`gsl::cdf::``pareto_Q` x a b

  implements `gsl_cdf_pareto_Q`.

`gsl::cdf::``pareto_Pinv` p a b

  implements `gsl_cdf_pareto_Pinv`.

`gsl::cdf::``pareto_Qinv` q a b

  implements `gsl_cdf_pareto_Qinv`.

`gsl::cdf::``weibull_P` x a b

  implements `gsl_cdf_weibull_P`.

`gsl::cdf::``weibull_Q` x a b

  implements `gsl_cdf_weibull_Q`.

`gsl::cdf::``weibull_Pinv` p a b

  implements `gsl_cdf_weibull_Pinv`.

`gsl::cdf::``weibull_Qinv` q a b

  implements `gsl_cdf_weibull_Qinv`.

`gsl::cdf::``gumbel1_P` x a b

  implements `gsl_cdf_gumbel1_P`.

`gsl::cdf::``gumbel1_Q` x a b

  implements `gsl_cdf_gumbel1_Q`.

`gsl::cdf::``gumbel1_Pinv` p a b

  implements `gsl_cdf_gumbel1_Pinv`.

`gsl::cdf::``gumbel1_Qinv` q a b

  implements `gsl_cdf_gumbel1_Qinv`.

`gsl::cdf::``gumbel2_P` x a b

  implements `gsl_cdf_gumbel2_P`.

`gsl::cdf::``gumbel2_Q` x a b

  implements `gsl_cdf_gumbel2_Q`.

`gsl::cdf::``gumbel2_Pinv` p a b

  implements `gsl_cdf_gumbel2_Pinv`.

`gsl::cdf::``gumbel2_Qinv` q a b

  implements `gsl_cdf_gumbel2_Qinv`.

`gsl::cdf::``poisson_P` k::int mu

  implements `gsl_cdf_poisson_P`.

`gsl::cdf::``poisson_Q` k::int mu

  implements `gsl_cdf_poisson_Q`.

`gsl::cdf::``binomial_P` k::int p n::int

  implements `gsl_cdf_binomial_P`.

`gsl::cdf::``binomial_Q` k::int q n::int

  implements `gsl_cdf_binomial_Q`.

`gsl::cdf::``negative_binomial_P` k::int p n

  implements `gsl_cdf_negative_binomial_P`.

`gsl::cdf::``negative_binomial_Q` k::int p n

  implements `gsl_cdf_negative_binomial_Q`.

`gsl::cdf::``pascal_P` k::int p n::int

  implements `gsl_cdf_pascal_P`.

`gsl::cdf::``pascal_Q` k::int p n::int

  implements `gsl_cdf_pascal_Q`.

`gsl::cdf::``geometric_P` k::int p

  implements `gsl_cdf_geometric_P`.

`gsl::cdf::``geometric_Q` k::int p

  implements `gsl_cdf_geometric_Q`.

`gsl::cdf::``hypergeometric_P` k::int n1::int n2::int t::int

  implements `gsl_cdf_hypergeometric_P`.

`gsl::cdf::``hypergeometric_Q` k::int n1::int n2::int t::int

  implements `gsl_cdf_hypergeometric_Q`.

### Examples

The following illustrates the use of each function in the `randist` module. The pdf functions are illustrated first.

    > using gsl::stats;
    > using namespace gsl::ran;
    > ugaussian_pdf 1.2;
    0.194186054983213
    > gaussian_pdf (-1.3) 1.5;
    0.182690978264686
    > gaussian_tail_pdf 2.0 1.0 1.5;
    0.433042698395299
    > ugaussian_tail_pdf 2.0 1.0;
    0.34030367841782
    > bivariate_gaussian_pdf 1.2 0.9 1.0 1.0 0.95;
    0.184646843689817
    > exponential_pdf 1.0 0.5;
    0.270670566473225
    > laplace_pdf 1.5 2.0;
    0.118091638185254
    > exppow_pdf 0.0 1.0 1.5;
    0.553866083716236
    > cauchy_pdf (-1.0) 1.0;
    0.159154943091895
    > rayleigh_pdf 2.5 1.0;
    0.109842334058519
    > rayleigh_tail_pdf 1.5 1.0 1.0;
    0.802892142778485
    > landau_pdf 1.1;
    0.140968737919623
    > gamma_pdf 1.0 1.0 1.5;
    0.342278079355061
    > flat_pdf 1.0 0.5 2.5;
    0.5
    > lognormal_pdf 0.01 0.0 1.0;
    0.000990238664959182
    > chisq_pdf 1.0 2.0;
    0.303265329856317
    > fdist_pdf 0.5 3.0 2.0;
    0.480970043785452
    > tdist_pdf 0.1 10.0;
    0.386975225815181
    > beta_pdf 0.5 4.0 1.0;
    0.499999999999999
    > logistic_pdf (-1.0) 2.0;
    0.117501856100797
    > pareto_pdf 0.01 3.0 2.0;
    0.0
    > weibull_pdf  0.01 1.0 1.0;
    0.990049833749168
    > gumbel1_pdf 0.01 1.0 1.0;
    0.367861108816436
    > gumbel2_pdf 0.01 1.0 1.0;
    3.72007597602084e-40
    > dirichlet_pdf {0.1,0.2,0.8} {2.0,2.0,2.0};
    0.00501316294425874
    > dirichlet_lnpdf {0.1,0.2,0.8} {2.0,2.0,2.0};
    -5.29568823688856
    > poisson_pdf 4 0.4;
    0.000715008049104682
    > bernoulli_pdf 1 0.7;
    0.7
    > binomial_pdf 3 0.5 9;
    0.1640625
    > multinomial_pdf {0.1,0.2,0.7} {2,2,2};
    0.0
    > multinomial_lnpdf {0.1,0.2,0.7} {2,2,2};
    -1728120799.71174
    > negative_binomial_pdf 10 0.5 3.5;
    0.0122430486923836
    > pascal_pdf 10 0.5 3;
    0.00805664062499999
    > geometric_pdf 5 0.4;
    0.05184
    > hypergeometric_pdf 1 5 20 3;
    0.413043478260872
    > logarithmic_pdf 10 0.7;
    0.00234619293712492
    > test_discrete
    >   = v
    >     when
    >       px = discrete_preproc {0.1,0.3,0.4};
    >       v = discrete_pdf 0 px +
    >           discrete_pdf 1 px +
    >           discrete_pdf 2 px;
    >       _ = discrete_free px
    >     end;
    > test_discrete;
    1.0

The cumulative distribution functions are shown.

    > using namespace gsl::cdf;
    > ugaussian_P  (-1.3);
    0.0968004845856103
    > ugaussian_Q  (-1.3);
    0.90319951541439
    > ugaussian_Pinv  0.84;
    0.994457883209753
    > ugaussian_Qinv  0.84;
    -0.994457883209753
    > gaussian_P  (1.3)  1.5;
    0.806937662858093
    > gaussian_Q  (1.3)  1.5;
    0.193062337141907
    > gaussian_Pinv  0.4  5.0;
    -1.266735515679
    > gaussian_Qinv  0.4 5.0;
    1.266735515679
    > exponential_P  1.0  0.5;
    0.864664716763387
    > exponential_Q  1.0  0.5;
    0.135335283236613
    > exponential_Pinv  0.6  0.5;
    0.458145365937077
    > exponential_Qinv  0.6  0.5;
    0.255412811882995
    > laplace_P  1.5  2.0;
    0.763816723629493
    > laplace_Q  1.5  2.0;
    0.236183276370507
    > laplace_Pinv  0.6  2.0;
    0.446287102628419
    > laplace_Qinv  0.4  2.0;
    0.446287102628419
    > exppow_P  0.0  1.0  2.5;
    0.5
    > exppow_Q  0.0  1.0  0.5;
    0.5
    > cauchy_P  (-1.0)  1.0;
    0.25
    > cauchy_Q  (-1.0)  1.0;
    0.75
    > cauchy_Pinv  0.75  1.0;
    1.0
    > cauchy_Qinv  0.25  1.0;
    1.0
    > rayleigh_P  1.5  2.0;
    0.245160398010993
    > rayleigh_Q  0.5  1.0;
    0.882496902584595
    > rayleigh_Pinv  0.5  1.0;
    1.17741002251547
    > rayleigh_Qinv  0.5  1.0;
    1.17741002251547
    > gamma_P  1.0  1.0  3.0;
    0.283468689426211
    > gamma_Q  1.0  1.0  3.0;
    0.716531310573789
    > gamma_Pinv  0.5  1.0  1.0;
    0.693147180559945
    > gamma_Qinv  0.5  1.0  1.0;
    0.693147180559945
    > flat_P  2.0  1.2  4.8;
    0.222222222222222
    > flat_Q  2.0  1.2  4.8;
    0.777777777777778
    > flat_Pinv  0.2  0.5  2.5;
    0.9
    > flat_Qinv  0.2  0.5  2.5;
    2.1
    > lognormal_P  0.01  0.0  1.0;
    2.06064339597172e-06
    > lognormal_Q  0.01  0.0  1.0;
    0.999997939356604
    > lognormal_Pinv  0.1  0.0  1.0;
    0.27760624185201
    > lognormal_Qinv  0.1  0.0  1.0;
    3.60222447927916
    > chisq_P  1.0  2.0;
    0.393469340287367
    > chisq_Q  1.0  2.0;
    0.606530659712633
    > chisq_Pinv  0.5  2.0;
    0.221199216928595
    > chisq_Qinv  0.5  2.0;
    1.38629436111989
    > fdist_P  1.0  3.0  2.0;
    0.46475800154489
    > fdist_Q  1.0  3.0  2.0;
    0.53524199845511
    > fdist_Pinv  0.5  3.0  2.0;
    1.13494292261288
    > fdist_Qinv  0.5  3.0  2.0;
    1.13494292261288
    > tdist_P  2.1  10.0;
    0.968961377898891
    > tdist_Q  (-2.1)  10.0;
    0.968961377898891
    > tdist_Pinv  0.68  10.0;
    0.482264205919689
    > tdist_Qinv  0.68  10.0;
    -0.482264205919689
    > beta_P  0.75  2.0  2.0;
    0.84375
    > beta_Q  0.75  2.0  2.0;
    0.15625
    > beta_Pinv  0.75  2.0  2.0;
    0.673648177666931
    > beta_Qinv  0.25  2.0  2.0;
    0.673648177666931
    > logistic_P  (-1.0)  2.0;
    1
    > logistic_Q  (-1.0)  2.0;
    0.622459331201855
    > logistic_Pinv  0.75  1.0;
    1.09861228866811
    > logistic_Qinv  0.25  1.0;
    1.09861228866811
    > pareto_P  2.01  3.0  2.0;
    0.0148512406901899
    > pareto_Q  2.01  3.0  2.0;
    0.98514875930981
    > pareto_Pinv  0.1  3.0  2.0;
    2.07148833730257
    > pareto_Qinv  0.1  3.0  2.0;
    4.30886938006377
    > weibull_P  1.01  1.0  2.0;
    0.639441117518024
    > weibull_Q  1.01  2.0  3.0;
    0.879160657465162
    > weibull_Pinv  0.1  1.0  2.0;
    0.324592845974501
    > weibull_Qinv  0.1  1.0  2.0;
    1.51742712938515
    > gumbel1_P  1.01  1.0  1.0;
    0.694739044426344
    > gumbel1_Q  1.01  1.0  1.0;
    0.305260955573656
    > gumbel1_Pinv  0.1  1.0  1.0;
    -0.834032445247956
    > gumbel1_Qinv  0.1  1.0  1.0;
    2.25036732731245
    > gumbel2_P  1.01  1.0  1.0;
    0.371539903071873
    > gumbel2_Q  1.01  1.0  1.0;
    0.628460096928127
    > gumbel2_Pinv  0.1  1.0  1.0;
    0.434294481903252
    > gumbel2_Qinv  0.1  1.0  1.0;
    9.4912215810299
    > poisson_P  4  0.4;
    0.999938756672898
    > poisson_Q  4  0.6;
    0.000394486018340255
    > binomial_P  3  0.5  10;
    0.171874999999999
    > binomial_Q  3  0.5  10;
    0.828125000000001
    > negative_binomial_P  10  0.5  3.0;
    0.98876953125
    > negative_binomial_Q  10  0.5  3.0;
    0.01123046875
    > pascal_P  10  0.5  3;
    0.98876953125
    > pascal_Q  10  0.5  3;
    0.01123046875
    > geometric_P  5  0.4;
    0.92224
    > geometric_Q  5  0.6;
    0.01024
    > hypergeometric_P  1  5  20  3;
    0.908695652173913
    > hypergeometric_Q  1  5  20  3;
    0.0913043478260873

## Sorting

This module is loaded via the command `using gsl::sort` and provides Pure wrappers for the GSL sorting routines found in Chapter 11 of the GSL manual,

http://www.gnu.org/software/gsl/manual/html_node/Sorting.html.

### Routines

`gsl::``sort_vector` m::matrix

  implements `gsl_sort` and `gsl_sort_int` without `stride` and `n` parameters.

`gsl::``sort_vector_index` m::matrix

  implements `gsl_sort_index` and `gsl_sort_int_index` without `stride` and `n` parameters.

### Examples

Usage of each library routine is illustrated below.

    > using gsl::sort;
    > using namespace gsl;
    > sort_vector {0,3,2,4,5};
    {0,2,3,4,5}
    > sort_vector_index {0.0,1.0,5.0,2.0,8.0,0.0};
    {5,0,1,3,2,4}

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