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Functions for Learning Bayesian Inference
LearnBayes contains a collection of functions helpful in learning
the basic tenets of Bayesian statistical inference. It contains
functions for summarizing basic one and two parameter posterior
distributions and predictive distributions. It contains MCMC
algorithms for summarizing posterior distributions defined by the
user. It also contains functions for regression models, hierarchical
models, Bayesian tests, and illustrations of Gibbs sampling.
Modelling with Sparse And Dense Matrices
Modelling with sparse and dense 'Matrix' matrices, using modular
prediction and response module classes.
Algorithms and Framework for Nonnegative Matrix Factorization
Provides a framework to perform Non-negative Matrix Factorization
(NMF). The package implements a set of already published algorithms
and seeding methods, and provides a framework to test, develop and
plug new/custom algorithms. Most of the built-in algorithms have
been optimized in C++, and the main interface function provides an
easy way of performing parallel computations on multicore machines.
Hidden Markov Models simulations and estimations
Simulations and estimations of discrete, univariate or multivariate
gaussian, mixture of univariate or multivariate gaussian hidden
Markov models.
R SVG graphics device
A graphics device for R that uses the w3.org xml standard
for Scalable Vector Graphics.
The gmp shared extension for php
Deep Learning library for Python
Deep Learning library for Python. Convnets, recurrent neural networks, and
more. Runs on Theano and TensorFlow.
R wrapper for the mpfr multiple precision library
Arithmetic (via S4 classes and methods) for arbitrary precision
floating point numbers, including transcendental ("special")
functions. To this end, Rmpfr interfaces to the LGPL'ed MPFR (Multiple
Precision Floating-Point Reliable) Library which itself is based
on the GMP (GNU Multiple Precision) Library.
Routines for Block Diagonal Symmetric matrices
This is a special case of sparse matrices, used by coxme
Python interface to the R Programming Language
RPy is a very simple, yet robust, Python interface to the R Programming
Language. It can manage all kinds of R objects and can execute arbitrary
R functions (including the graphic functions). All the errors from the
R language are converted to Python exceptions. Any module that later were
installed on the R system, can easily be used from within Python, without
introducing any changes.
rpy2 is a redesign and rewrite of rpy. It is providing a low-level interface
to R, a proposed high-level interface, including wrappers to graphical
libraries, as well as R-like structures and functions.