Objects			page : 1 2 3 4 5 6 7 8 9 10 11 12
lp.lonnie	External		Generate random numbers from a log-normal distribution The log-normal distribution is derived from the normal (or "Gaussian") distribution. By definition, if the logarithm of a set of random variables has a normal distribution, then the variable has a log-normal distribution. Conceptually, one can think of the lognormal distribution is as the product of many independent uniform distributions (in contrast to the normal distribution, which is derived from the notion of summing independent uniform distributions). The log-normal distribution is often used to model characteristics such as income distribution, distribution of grain sizes in geological contexts, and distribution of weight or height in biological contexts. The log-normal distribution has two parameters: mean and standard deviation. Values from a log-normal distribution are positive and skewed to the right (i.e., the median is greater than the arithmetic mean.)
lp.norm	External		Generate random numbers from a normal ( "Gaussian ") distribution
lp.pfff	External		Generate random numbers from a 1/f2 ( "Brownian ") distribution
lp.pfishie	External		Generate random numbers from a Poisson distribution The Poisson distribution has one parameter, É…, which happens to be both the expected mean and variance. (Standard deviation is therefore É…). The Poisson distribution generates non-negative integers only. It is defined for positive real values of É…. The Poisson distribution was originally developed as an efficient means of approximating the Bernoulli distribution for special cases (to wit, when the product np is small even when n is large). It has gained considerable popularity for use in algorithmic composition, particularly due to the influence of Iannis Xenakis, who used it extensively.
lp.ppp~	External		Popcorn (dust) noise This noise generator, known variously as popcorn or dust noise, generates exponentially distributed pulses of varying amplitude and pulse width. It resembles kinds of noise frequently found in telecommunications lines and sometimes in radio broadcast. Curiously, in most naturally occurring circumstances, the pulses are all of the same sign, either positive or negative. The lp.ppp~ object supports both, as well as a symmetrical variant in which positive and negative pulses are mixed at random. When the density of pops becomes high and pulse width also increases, it becomes possible for pops to overlap. The current implementation makes no provision for overlapping pops; one pop must be completed (i.e., the signal must return to 0) before the next one can begin. Thus, the actual frequency of pops may fall slightly underneath the specified mean.
lp.shhh	External		Generate random numbers from a "white " distribution
lp.shhh~	External		White noise This is the "whitest " white noise available for Max/MSP, taking about 2.2 · 1014 years to repeat its cycle. That’s an order of magnitude longer than the estimated age of the universe since the Big Bang. Based on the lp.tata random number generator, it should also use a little less processing power than other white noise implementations.
lp.sss	External		Generate random numbers from a 1/f ( "pink ") distribution (Voss/Gardner algorithm)
lp.sss~	External		"Pink " noise (Voss/Gardner algorithm) Pink noise generated based on the original Voss/Gardner algorithm for generating 1/f distributed random numbers.
lp.stu	External		Generate random numbers from Student’s t distribution The t distribution has one parameter, referred to as degrees of freedom. It produces an asymmetrical distribution of positive deviates. The degrees of freedom parameter is a positive integer. The t distribution was developed by the statistician William Gosset. At the time of publication Gosset was employed by the Guinness brewery, which did not allow employees to publish, so Gosset wrote under the pseudonym of Student. The rest is history.
lp.tata	External		Generate random numbers using the Tausworthe 88 algorithm The lp.tata object implements the Tausworthe 88 random number generator. This is currently the fastest algorithm that passes all standard statistical tests for randomness. It has a cycle of approximately 288 (that’s about 3 ÅE 1026) and generates random values across the entire range of 32-bit numbers (i.e., -2,147,483,648 < x <2,147,483,647). The lp.tata object allows you to scale the output to a given range.
lp.titi	External		Generate random numbers using the TT800 algorithm The lp.titi object implements the TT800 random number generator proposed by Makoto Matsumoto and Yoshiharu Kurita. This algorithm passes all standard statistical tests for randomness. It has a cycle of 2800 - 1 (that’s approximately 6 ÅE 10240) and generates random values across the entire range of 32-bit numbers (i.e., from -2,147,483,648 to 2,147,483,647).
lp.y	External		Generate random numbers from Weibull and Rayleigh distributios
lp.zzz	External		Generate random numbers from a 1/f ("pink") distribution (McCartney algorithm) This is a control-domain version of the lp.zzz~ signal generator. It generates values in the range 0 < x < 1. It is based on a variant of the classic Voss/Gardner algorithm developed by James McCartney.
make-choice-list	Abstraction		random based object Generates a list of integers chosen randomly between a min. and a max. value and a length between a min. and a max. list length. This list can be used later for random selection of elements using one of Gottfried M. Koenigs selection principles (like alea, series etc.)
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Libraries
ag.graular.suite	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='166'Adrian Gierakowski	The ag.granular.suite is a collection of Max/MSP patches for generalised granular sound processing and microsound composition written using FTM/Gabor libraries (developed at IRCAM) and encapsulated as Jamoma modules. Main features include: subsample accurate scheduling, multichannel output, granulation of multiple soundfiles at the same time (with interpolation of two sources per grain), parameter randomisation and sequencing, control via OSC, preset management, preset interpolation. Its modular architecture makes it possible to easily extend it with new algorithms for grain scheduling and parameter control.
KN-Lib 2.7	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='109'Roland Cahen	KN-Lib is a collection of everyday abstraction tools. It contains mouse and keyboards facilities, converters, calculation, random, interval and scale generators, midi utilities...etc (The old version is no longer available. If necessary it can be downloades at : ftp://ftp.forumnet.ircam.fr/pub/max/FAT/misc)
KnLib2.8.1	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='138'Roland Cahen	KN-Lib 2.8 is a collection of everyday abstraction tools. It contains mouse and keyboards facilities, converters, calculation, random, interval and scale generators, midi utilities...etc Most of them are finished, a few are in progress.
Litter Power Pro Package	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='54'Peter Castine	The Litter Power package consists of over 60 external objects, including a number of new MSP noise sources, externals that produce values from a wide variety of random number distributions, and externals for mutation and cross-synthesis.
Litter Power Starter Package	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='53'Peter Castine	The Litter Power Starter Pack consists of about two dozen external objects, including a number of new MSP noise sources, a wide variety of random number distributions, time-domain mutation, and several very useful utilities.
MaxAlea	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='96'Carl Faia	MaxAlea contains various objects for random distributions and functions. MaxAlea was begun as a Max port of an existing PatchWork Library created in 1991-2 by Mikhail Malt. While the distributions and functions found in MaxAlea are similar to those found in the Patchwork version ,there are many differences in their functioning. The environment of Patchwork is static and is not designed for real-time work. Part of the incentive for creating these objects to work with Max was to have a dynamic and real-time environment with which to experiment and work with these algorithms in a manner as simple and straightforward as possible. One can change variables and manipulate the output in many ways in real-time. There are several different versions of the various stochastic models/processes best presented in the now classic references by Denis Lorrain and Charles Dodge. Carl Faia has used a variety of sources for the creation of this library which include the Lorrain, Dodge and Malt implementations as well as sources found on the WorldWideWeb. The externals found in the package include several random distributions, examples of random walks and 1/f noise algorithms, as well as one or two utilities written specifically for the MaxAlea library. Carl Faia wanted to make a coherent collection (as he thought Malt had managed to do in PatchWork) of these various algorithms and provide an interface easily accessible using the Max environment for real-time control. All these algorithms have been created using a seeded version of the random function found in the standard AINSI library. That is, each time the function is first run there will always be a different set of random numbers (unlike the random funtions found in Max, PatchWork and other versions of random number generators).
Random Objects	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='78'Gary Lee Nelson	These are the collections of seedable random number generators that I wrote sometime in the early 1990's. These classic, OSX and Windows ports are thanks to Jeremy Bernste
vRand abstractions	debug: SELECT prenom, nom FROM auteurs RIGHT JOIN auteur_libraries USING (id_auteur) WHERE auteur_libraries.id_library='77'Gary Lee Nelson	These new objects assume that you have downloaded and installed one of the the externals from the Random Objects library. There are OS9, OSX and PC versions. (Thanks again to Jeremy Bernstein.) I have not tested these new abstractions in OS 9 or Windows and would appreciate hearing from anyone you can verify that they work.