## functions in random.i - r

random_ipq
```
random_ipq(ipq_model, dimlist)

returns an array of double values with the given DIMLIST (see array
function, nil for a scalar result).  The numbers are distributed
according to a piecewise linear function (possibly with power law
or exponential tails) specified by the IPQ_MODEL.  The "IPQ" stands
for "inverse piecewise quadratic", which the type of function
required to transform a uniform random deviate into the piecewise
linear distribution.  Use the ipq_setup function to compute
IPQ_MODEL.
Interpreted function, defined at i/random.i   line 179

```

random_n
```
random_n(dimlist)

returns an array of normally distributed random double values with
the given DIMLIST (see array function, nil for a scalar result).
The mean is 0.0 and the standard deviation is 1.0.
The algorithm follows the Box-Muller method (see Numerical Recipes
by Press et al.).
Interpreted function, defined at i/random.i   line 130

```

random_rej
```
random_rej(target_dist, ipq_model, dimlist)
or random_rej(target_dist, bounding_dist, bounding_rand, dimlist)

returns an array of double values with the given DIMLIST (see array
function, nil for a scalar result).  The numbers are distributed
according to the TARGET_DIST function:
func target_dist(x)
returning u(x)>=0 of same number and dimensionality as x, normalized
so that the integral of target_dist(x) from -infinity to +infinity
is 1.0.  The BOUNDING_DIST function must have the same calling
sequence as TARGET_DIST:
func bounding_dist(x)
returning b(x)>=u(x) everywhere.  Since u(x) is normalized, the
integral of b(x) must be >=1.0.  Finally, BOUNDING_RAND is a
function which converts an array of uniformly distributed random
numbers on (0,1) -- as returned by random -- into an array
distributed according to BOUNDING_DIST:
func bounding_rand(uniform_x_01)
Mathematically, BOUNDING_RAND is the inverse of the integral of
BOUNDING_DIST from -infinity to x, with its input scaled to (0,1).
If BOUNDING_DIST is not a function, then it must be an IPQ_MODEL
returned by the ipq_setup function.  In this case BOUNDING_RAND is
omitted -- ipq_compute will be used automatically.
Interpreted function, defined at i/random.i   line 199

```

random_u
```
random_u(a, b, dimlist)

return uniformly distributed random numbers between A and B.
(Will never exactly equal A or B.)  The DIMLIST is as for the
array function.  Same as (b-a)*random(dimlist)+a.  If A==0,
you are better off just writing B*random(dimlist).
Interpreted function, defined at i/random.i   line 113

```

random_x
```
random_x(dimlist)

same as random(DIMLIST), except that random_x calls random
twice at each point, to avoid the defect that random only
can produce about 2.e9 numbers on the interval (0.,1.) (see
random for an explanation of these bins).
You may set random=random_x to get these "better" random
numbers in every call to random.
Unlike random, there is a chance in 1.e15 or so that random_x
may return exactly 1.0 or 0.0 (the latter may not be possible
with IEEE standard arithmetic, while the former apparently is).
Since cosmic rays are far more likely, you may as well not