Provides functions to compute the main characteristics of the
following queueing models: M/M/1, M/M/s, M/M/1/k, M/M/s/k, M/M/1/Inf/H, M/
M/s/Inf/H, M/M/s/Inf/H with Y replacements, M/M/Inf, Open Jackson Networks
and Closed Jackson Networks. Moreover, it is also possible to simulate
similar queueing models with any type of arrival or service distribution: G/
G/1, G/G/s, G/G/1/k, G/G/s/k, G/G/1/Inf/H, G/G/s/Inf/H, G/G/s/Inf/H with Y
replacements, Open Networks and Closed Networks. Finally, contains functions
for fit data to a statistic distribution.

graphics, stats, utils, methods, reshape, iterators,
doParallel, foreach, fitdistrplus, grid, gridExtra

Apply a Function to a Margin of an Array
High performance variant of apply() for a fixed set of functions.
However, a considerable speedup is a trade-off for universality.
Only the following functions can be applied: sum(), prod(), all(), any(),
min(), max(), mean(), median(), sd(), var().

High performance variant of apply() for a fixed set of functions.
Considerable speedup is a trade-off for universality, user defined
functions cannot be used with this package. However, 20 most currently employed
functions are available for usage. They can be divided in three types:
reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
mapping function (like normalise(), cumsum() etc., giving a vector of the same length
as the input vector) and finally, vector reducing function (like diff() which produces
result vector of a length different from the length of input vector).
Optional or mandatory additional arguments required by some functions
(e.g. norm type for norm() or normalise() functions) can be
passed as named arguments in '...'.

High performance variant of apply() for a fixed set of functions.
Considerable speedup is a trade-off for universality, user defined
functions cannot be used with this package. However, 21 most currently employed
functions are available for usage. They can be divided in three types:
reducing functions (like mean(), sum() etc., giving a scalar when applied to a vector),
mapping function (like normalise(), cumsum() etc., giving a vector of the same length
as the input vector) and finally, vector reducing function (like diff() which produces
result vector of a length different from the length of input vector).
Optional or mandatory additional arguments required by some functions
(e.g. norm type for norm() or normalise() functions) can be
passed as named arguments in '...'.