Debian Science Project
Summary
Machine Learning
Debian Science Machine Learning packages

This metapackage will install packages useful for machine learning. Included packages range from knowledge-based (expert) inference systems to software implementing the advanced statistical methods that currently dominate the field.

Description

For a better overview of the project's availability as a Debian package, each head row has a color code according to this scheme:

If you discover a project which looks like a good candidate for Debian Science to you, or if you have prepared an unofficial Debian package, please do not hesitate to send a description of that project to the Debian Science mailing list

Links to other tasks

Debian Science Machine Learning packages

Official Debian packages with high relevance

autoclass
automatic classification or clustering
Versions of package autoclass
ReleaseVersionArchitectures
stretch3.3.6.dfsg.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.3.6.dfsg.2-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie3.3.6.dfsg.2-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm3.3.6.dfsg.2-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.3.6.dfsg.1-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie3.3.6.dfsg.1-1amd64,armel,armhf,i386
buster3.3.6.dfsg.1-1amd64,arm64,armhf,i386
Debtags of package autoclass:
fieldmathematics
interfacecommandline
roleprogram
scopeutility
useorganizing
Popcon: 3 users (0 upd.)*
Versions and Archs
License: DFSG free

AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering, or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the "natural" classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.

caffe-cpu
Fast, open framework for Deep Learning (Meta)
Versions of package caffe-cpu
ReleaseVersionArchitectures
buster1.0.0+git20180821.99bd997-2amd64,arm64,armhf,i386
stretch1.0.0~rc4-1amd64,arm64,armel,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley AI Research Lab (BAIR) and community contributors.

This metapackage pulls CPU_ONLY version of caffe:

  • caffe-tools-cpu
  • libcaffe-cpu*
  • python3-caffe-cpu And suggests these packages:

  • libcaffe-cpu-dev

  • caffe-doc

Note, this CPU_ONLY version cannot co-exist with the CUDA version.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)
Screenshots of package caffe-cpu
gprolog
компилятор GNU Prolog
Maintainer: Salvador Abreu
Versions of package gprolog
ReleaseVersionArchitectures
sid1.4.5.0-3amd64,i386
trixie1.4.5.0-3amd64,i386
jessie1.3.0-6.1amd64,i386
bookworm1.4.5.0-3amd64,i386
bullseye1.4.5.0-3amd64,i386
Debtags of package gprolog:
develcompiler, interpreter, lang:prolog
interfacecommandline
roleprogram
scopeutility
suitegnu
works-withsoftware:source
Popcon: 13 users (4 upd.)*
Versions and Archs
License: DFSG free

GNU Prolog — свободный компилятор Prolog с поддержкой поиска решений на основе заданных ограничений в конечных областях. GNU Prolog в значительной степени соответствует стандарту ISO и является частью инициативы Prolog Commons.

Пакет содержит компилятор и систему исполнения GNU Prolog, соответствующую стандарту ISO и включающую реализацию прототипов модулей.

libcv-dev
??? missing short description for package libcv-dev :-(
Maintainer: Debian Science Team
Versions of package libcv-dev
ReleaseVersionArchitectures
jessie-security2.4.9.1+dfsg-1+deb8u2amd64,armel,armhf,i386
stretch-security2.4.9.1+dfsg1-2+deb9u1amd64,arm64,armel,armhf,i386
stretch2.4.9.1+dfsg1-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie2.4.9.1+dfsg-1+deb8u1amd64,armel,armhf,i386
upstream4.10.0
Debtags of package libcv-dev:
devellibrary
roledevel-lib
Popcon: 1 users (0 upd.)*
Newer upstream!
License: DFSG free
Git
Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
Registry entries: SciCrunch 
libevocosm-dev
??? missing short description for package libevocosm-dev :-(
Maintainer: Al Stone (Chris Lamb)
Versions of package libevocosm-dev
ReleaseVersionArchitectures
jessie4.0.2-3amd64,armel,armhf,i386
stretch4.0.2-3.1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package libevocosm-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
libfann-dev
Development libraries and header files for FANN
Maintainer: Christian Kastner
Versions of package libfann-dev
ReleaseVersionArchitectures
trixie2.2.0+ds-8amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid2.2.0+ds-8amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
jessie2.1.0~beta+dfsg-1amd64,armel,armhf,i386
buster2.2.0+ds-5amd64,arm64,armhf,i386
stretch2.2.0+ds-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bookworm2.2.0+ds-8amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye2.2.0+ds-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package libfann-dev:
devellang:c, library
roledevel-lib
Popcon: 5 users (7 upd.)*
Versions and Archs
License: DFSG free
Git

Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast.

This package contains the header files and static libraries which are needed for developing libfann applications.

libga-dev
C++ Library of Genetic Algorithm Components
Versions of package libga-dev
ReleaseVersionArchitectures
bookworm2.4.7-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.4.7-4amd64,arm64,armhf,i386
sid2.4.7-6amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
stretch2.4.7-4amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie2.4.7-3.1amd64,armel,armhf,i386
bullseye2.4.7-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie2.4.7-6amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Debtags of package libga-dev:
devellibrary
roledevel-lib
Popcon: 2 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. The documentation includes an extensive overview of how to implement a genetic algorithm as well as examples illustrating customizations to the GAlib classes.

This package contains the development files.

liblinear-dev
Development libraries and header files for LIBLINEAR
Versions of package liblinear-dev
ReleaseVersionArchitectures
stretch2.1.0+dfsg-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
trixie2.3.0+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid2.3.0+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bullseye2.3.0+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.1.0+dfsg-4amd64,arm64,armhf,i386
bookworm2.3.0+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie1.8+dfsg-4amd64,armel,armhf,i386
experimental2.43+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
upstream2.4.7
Debtags of package liblinear-dev:
devellang:c, lang:c++, library
roledevel-lib
Popcon: 5 users (3 upd.)*
Newer upstream!
License: DFSG free
Git

LIBLINEAR is a library for learning linear classifiers for large scale applications. It supports Support Vector Machines (SVM) with L2 and L1 loss, logistic regression, multi class classification and also Linear Programming Machines (L1-regularized SVMs). Its computational complexity scales linearly with the number of training examples making it one of the fastest SVM solvers around.

This package contains the header files and static libraries.

libmlpack-dev
intuitive, fast, scalable C++ machine learning library (development libs)
Versions of package libmlpack-dev
ReleaseVersionArchitectures
trixie4.5.0-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bullseye3.4.2-1amd64,arm64,i386,ppc64el,s390x
buster3.0.4-1amd64,arm64,armhf,i386
stretch2.1.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie1.0.10-1amd64,armel,armhf,i386
sid4.5.0-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package contains the mlpack Library development files.

Machine Learning Pack (mlpack) is an intuitive, fast, scalable C++ machine learning library, meant to be a machine learning analog to LAPACK. It aims to implement a wide array of machine learning methods and function as a "swiss army knife" for machine learning researchers.

libocas-dev
Development libraries and header files for LIBOCAS
Maintainer: Christian Kastner
Versions of package libocas-dev
ReleaseVersionArchitectures
jessie0.97-1amd64,armel,armhf,i386
bookworm0.97+dfsg-8amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie0.97+dfsg-8amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid0.97+dfsg-8amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
stretch0.97+dfsg-3amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster0.97+dfsg-5amd64,arm64,armhf,i386
bullseye0.97+dfsg-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package libocas-dev:
devellang:c, library
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This library implements Optimized Cutting Plane Algorithm (OCAS) for training linear Support Vector Machine (SVM) classifiers from large-scale data. The computational effort of OCAS scales linearly with the number of training examples. It is one of the fastest SVM solvers around for solving linear and multiclass L2 regularized SVMs.

This package contains the header files and static libraries.

libroot-math-mlp-dev
??? missing short description for package libroot-math-mlp-dev :-(
Versions of package libroot-math-mlp-dev
ReleaseVersionArchitectures
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
Debtags of package libroot-math-mlp-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
libroot-montecarlo-vmc-dev
??? missing short description for package libroot-montecarlo-vmc-dev :-(
Versions of package libroot-montecarlo-vmc-dev
ReleaseVersionArchitectures
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
Debtags of package libroot-montecarlo-vmc-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
libroot-tmva-dev
??? missing short description for package libroot-tmva-dev :-(
Versions of package libroot-tmva-dev
ReleaseVersionArchitectures
jessie5.34.19+dfsg-1.2amd64,armel,armhf,i386
Debtags of package libroot-tmva-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
libshark-dev
??? missing short description for package libshark-dev :-(
Versions of package libshark-dev
ReleaseVersionArchitectures
stretch3.1.3+ds1-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
Please cite: Luca Denti, Yuri Pirola, Marco Previtali, Tamara Ceccato, Gianluca Della Vedova, Raffaella Rizzi and Paola Bonizzoni: Shark: fishing relevant reads in an RNA-Seq sample. (eprint) Bioinformatics 37(4):464-472 (2020)
Registry entries: Bio.tools  Bioconda 
libshogun-dev
Large Scale Machine Learning Toolbox
Versions of package libshogun-dev
ReleaseVersionArchitectures
buster3.2.0-8amd64,arm64,armhf,i386
jessie3.2.0-7.3amd64,armel,armhf,i386
Debtags of package libshogun-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Svn

SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing.

SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables.

libsvm-dev
LIBSVM header files
Versions of package libsvm-dev
ReleaseVersionArchitectures
experimental3.25+ds-1~exp1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid3.24+ds-6amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie3.24+ds-6amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm3.24+ds-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.24+ds-6amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster3.21+ds-1.2amd64,arm64,armhf,i386
stretch3.21+ds-1.1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie3.12-1amd64,armel,armhf,i386
upstream3.35
Debtags of package libsvm-dev:
devellibrary
roledevel-lib
Popcon: 11 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

LIBSVM, a machine-learning library, is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection.

This package contains the development header files.

libtorch3-dev
State of the art machine learning library - development files
Versions of package libtorch3-dev
ReleaseVersionArchitectures
jessie3.1-2.1amd64,armel,armhf,i386
buster3.1-2.2amd64,arm64,armhf,i386
stretch3.1-2.2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package libtorch3-dev:
devellibrary
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free

Torch is a machine-learning library, written in C++. Its aim is to provide the state-of-the-art of the best algorithms.

  • Many gradient-based methods, including multi-layered perceptrons, radial basis functions, and mixtures of experts. Many small "modules" (Linear module, Tanh module, SoftMax module, ...) can be plugged together.
  • Support Vector Machine, for classification and regression.
  • Distribution package, includes Kmeans, Gaussian Mixture Models, Hidden Markov Models, and Bayes Classifier, and classes for speech recognition with embedded training.
  • Ensemble models such as Bagging and Adaboost.
  • Non-parametric models such as K-nearest-neighbors, Parzen Regression and Parzen Density Estimator.

This package is the Torch development package (header files and static library.)

libvigraimpex-dev
development files for the C++ computer vision library
Versions of package libvigraimpex-dev
ReleaseVersionArchitectures
jessie1.9.0+dfsg-10amd64,armel,armhf,i386
sid1.12.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie1.12.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm1.11.1+dfsg-11amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye1.11.1+dfsg-8amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.10.0+git20160211.167be93+dfsg1-2amd64,arm64,armhf,i386
stretch1.10.0+git20160211.167be93+dfsg-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Debtags of package libvigraimpex-dev:
devellang:c++, library
roledevel-lib
works-withimage, image:raster
Popcon: 1 users (6 upd.)*
Versions and Archs
License: DFSG free
Git

Vision with Generic Algorithms (VIGRA) is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principle know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment.

This package contains the header and development files needed to build programs and packages using VIGRA.

lua-torch-graph
Graph Computation Package for Torch Framework
Versions of package lua-torch-graph
ReleaseVersionArchitectures
buster0~20161121-g37dac07-3all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides graphical computation for Torch.

This package also ships a graphviz interface, you need not graphviz to be able to use this library but, if you have it, you will be able to display the graphs that you have created.

lua-torch-image
Image Load/Save Library for Torch Framework
Versions of package lua-torch-image
ReleaseVersionArchitectures
buster0~20170420-g5aa1881-7amd64,armhf
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

"image" is the Torch7 distribution package for processing images. It contains a wide variety of functions divided into the following categories:

  • Saving and loading images as JPEG, PNG, PPM and PGM;
  • Simple transformations like translation, scaling and rotation;
  • Parameterized transformations like convolutions and warping;
  • Simple Drawing Routines like drawing text or a rectangle on an image;
  • Graphical user interfaces like display and window;
  • Color Space Conversions from and to RGB, YUV, Lab, and HSL;
  • Tensor Constructors for creating Lenna, Fabio and Gaussian and Laplacian kernels;

Note that unless specified otherwise, this package deals with images of size nChannel x height x width.

lua-torch-nn
Neural Network Package for Torch Framework
Versions of package lua-torch-nn
ReleaseVersionArchitectures
buster0~20171002-g8726825+dfsg-4all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides an easy and modular way to build and train simple or complex neural networks using Torch Framework:

  • Modules are the bricks used to build neural networks. Each are themselves neural networks, but can be combined with other networks using containers to create complex neural networks:

  • Module: abstract class inherited by all modules.

  • Containers: container classes.
  • Transfer functions: non-linear functions.
  • Simple layers: simple network layer like Linear.
  • Table layers: layers for manipulating tables.
  • Convolution layers: several kinds of convolutions.

  • Criterions compute a gradient according to a given loss function given an input and a target:

  • Criterions: a list of all criterions.

  • MSECriterion: the Mean Squared Error criterion used for regression;
  • ClassNLLCriterion: the Negative Log Likelihood criterion used for classification.

  • Additional documentation:

  • Overview of the package essentials including modules, containers and training.

  • Training: how to train a neural network using optim.
  • Testing: how to test your modules.
  • Experimental Modules: a package containing experimental modules and criteria.

This package is a core part of the Torch Framework.

lua-torch-nngraph
Neural Network Graph Package for Torch Framework
Versions of package lua-torch-nngraph
ReleaseVersionArchitectures
buster0~20170208-g3ed3b9b-3all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides graphical computation for nn library in Torch. The aim of this library is to provide users of nn package with tools to easily create complicated architectures. Any given nn module is going to be bundled into a graph node. The __call__ operator of an instance of nn.Module is used to create architectures as if one is writing function calls.

lua-torch-optim
Numeric Optimization Package for Torch Framework
Versions of package lua-torch-optim
ReleaseVersionArchitectures
buster0~20171127-ga5ceed7-1all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package contains several optimization routines and a logger for Torch.

The following algorithms are provided:

  • Stochastic Gradient Descent
  • Averaged Stochastic Gradient Descent
  • L-BFGS
  • Congugate Gradients
  • AdaDelta
  • AdaGrad
  • Adam
  • AdaMax
  • FISTA with backtracking line search
  • Nesterov's Accelerated Gradient method
  • RMSprop
  • Rprop
  • CMAES All these algorithms are designed to support batch optimization as well as stochastic optimization. It's up to the user to construct an objective function that represents the batch, mini-batch, or single sample on which to evaluate the objective.

This package provides also logging and live plotting capabilities via the optim.Logger() function. Live logging is essential to monitor the network accuracy and cost function during training and testing, for spotting under- and over-fitting, for early stopping or just for monitoring the health of the current optimisation task.

lua-torch-trepl
REPL Package for Torch Framework
Versions of package lua-torch-trepl
ReleaseVersionArchitectures
buster0~20170619-ge5e17e3-7amd64,armhf,i386
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

A pure Lua REPL (Read,Eval,Print-Loop) for LuaJIT, with heavy support for Torch types. It uses Readline for tab completion.

This package contains backend files to support the command line frontend 'th'.

lua-torch-xlua
Lua Extension Package for Torch Framework
Versions of package lua-torch-xlua
ReleaseVersionArchitectures
buster0~20160719-g41308fe-7all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Lua is pretty compact in terms of built-in functionalities: this package extends the table and string libraries, and provide other general purpose tools (progress bar, ...).

This package ships a set of useful extensions to Lua for Torch Framework.

mcl
кластерный алгоритм Маркова
Versions of package mcl
ReleaseVersionArchitectures
bullseye14-137+ds-9amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie14-137-1amd64,armel,armhf,i386
stretch14-137-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster14-137+ds-3amd64,arm64,armhf,i386
bookworm22-282+ds-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie22-282+ds-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid22-282+ds-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Debtags of package mcl:
fieldmathematics
roleprogram
Popcon: 8 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

В пакете, кроме реализации алгоритма, содержатся также утилиты для работы с разреженными матрицами (ключевая структура данных в алгоритме MCL) и экспериментирования с кластерами.

Алгоритм MCL применяется в таких областях, как биология (определение семей протеинов, геномика), ИТ (децентрализованные сети) и лингвистика (анализ текстов).

The package is enhanced by the following packages: zoem
Please cite: Stijn van Dongen and Cei Abreu-Goodger: Using MCL to extract clusters from networks. (PubMed,eprint) Methods Mol Biol. 804:281-95 (2012)
Registry entries: Bio.tools 
mrgingham
Chessboard finder for visual calibration routines
Versions of package mrgingham
ReleaseVersionArchitectures
sid1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm1.22-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: 1 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the user-facing tools

octave-ga
genetic optimization code for Octave
Versions of package octave-ga
ReleaseVersionArchitectures
bookworm0.10.3-2all
jessie0.10.0-2all
stretch0.10.0-2all
sid0.10.4-1all
buster0.10.0-6all
bullseye0.10.2-1all
trixie0.10.4-1all
Debtags of package octave-ga:
devellang:octave, library
roledevel-lib
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides function to work with genetic algorithms in Octave, a numerical computation software. It provides the ga() function, which works similarly to other optimization functions in Octave.

This Octave add-on package is part of the Octave-Forge project.

pgapack
??? missing short description for package pgapack :-(
Maintainer: Dirk Eddelbuettel
Versions of package pgapack
ReleaseVersionArchitectures
jessie1.1.1-3amd64,armel,armhf,i386
Debtags of package pgapack:
fieldmathematics
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Screenshots of package pgapack
python3-amp
Atomistic Machine-learning Package (python 3)
Versions of package python3-amp
ReleaseVersionArchitectures
bullseye0.6.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bookworm0.6.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid0.6.1-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster0.6.1-1amd64,arm64,armhf,i386
upstream4878fc892f2cbc5cd9f29f7a367d7b05bdeb6ee9
Popcon: 7 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

Amp is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License. Amp allows for the modular representation of the potential energy surface, allowing the user to specify or create descriptor and regression methods.

Amp is designed to integrate closely with the Atomic Simulation Environment (ASE). As such, the interface is in pure python, although several compute-heavy parts of the underlying code also have fortran versions to accelerate the calculations. The close integration with ASE means that any calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem, and Gaussian ─ can easily be used as the parent method.

This package provides the python 3 modules.

python3-fann2
Python 3 bindings for FANN
Versions of package python3-fann2
ReleaseVersionArchitectures
stretch1.0.7-6amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
trixie1.2.0+ds-4amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid1.2.0+ds-4amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster1.1.2+ds-1amd64,arm64,armhf,i386
bullseye1.2.0+ds-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bookworm1.2.0+ds-4amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 3 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks.

This package contains the Python 3 bindings for FANN.

python3-genetic
genetic algorithms in Python
Versions of package python3-genetic
ReleaseVersionArchitectures
trixie0.1.1b+git20170527.98255cb-4all
bullseye0.1.1b+git20170527.98255cb-2all
sid0.1.1b+git20170527.98255cb-4all
bookworm0.1.1b+git20170527.98255cb-3all
Popcon: 3 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Python3-genetic provides genetic algorithms for Python3, as often used in artificial intelligence. It should be able to solve any problem that consists in minimizing functions.

You'll find some demos using Genetic in this package, including an impressively simple program that provides a solution to the well-known TSP (Travelling Salesman Problem). Also, make sure to read demo/genetic_demo_2.py for the list of the special "magic" genes that make Genetic really fun and ... living !

python3-keras
deep learning framework running on Theano or TensorFlow
Versions of package python3-keras
ReleaseVersionArchitectures
sid2.3.1+dfsg2-1all
bullseye2.3.1+dfsg-3all
buster2.2.4-1all
upstream2.4.3
Popcon: 15 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation.

Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing).

It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian).

python3-lasagne
deep learning library build on the top of Theano (Python3 modules)
Versions of package python3-lasagne
ReleaseVersionArchitectures
stretch0.1+git20160728.8b66737-2all
buster0.1+git20181019.a61b76f-1all
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Lasagne is a Python library to build and train deep (multi-layered) artificial neural networks on the top of Theano (math expression compiler). In comparison to other abstraction layers for that like e.g. Keras, it abstracts Theano as little as possible.

Lasagne supports networks like Convolutional Neural Networks (CNN, mostly used for image recognition resp. classification) and the Long Short-Term Memory type (LSTM, a subtype of Recurrent Neural Networks, RNN).

This package contains the modules for Python 3.

python3-mdp
Modular toolkit for Data Processing
Versions of package python3-mdp
ReleaseVersionArchitectures
jessie3.3-2all
bullseye3.6-1.1all
bookworm3.6-2amd64,arm64,mips64el,ppc64el
sid3.6-8all
stretch3.5-1all
trixie3.6-8all
Popcon: 16 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers.

The package is enhanced by the following packages: python3-sklearn
python3-mlpy
high-performance Python package for predictive modeling
Versions of package python3-mlpy
ReleaseVersionArchitectures
bullseye3.5.0+ds-1.2all
sid3.5.0+ds-3all
bookworm3.5.0+ds-2all
Popcon: 37 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping.

mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators.

python3-opencv
Python 3 bindings for the computer vision library
Versions of package python3-opencv
ReleaseVersionArchitectures
bullseye4.5.1+dfsg-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster3.2.0+dfsg-6amd64,arm64,armhf,i386
bookworm4.6.0+dfsg-12amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie4.6.0+dfsg-14amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid4.6.0+dfsg-14amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
upstream4.10.0
Popcon: 229 users (172 upd.)*
Newer upstream!
License: DFSG free
Git

This package contains Python 3 bindings for the OpenCV (Open Computer Vision) library.

The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance.

OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction.

Please cite: Gary Bradski and Adrian Kaehler: Learning OpenCV: Computer Vision with the OpenCV Library (2008)
Registry entries: SciCrunch 
python3-sklearn
Python modules for machine learning and data mining - Python 3
Versions of package python3-sklearn
ReleaseVersionArchitectures
trixie1.4.2+dfsg-6all
sid1.4.2+dfsg-6all
stretch0.18-5all
buster0.20.2+dfsg-6all
bullseye0.23.2-5all
bookworm1.2.1+dfsg-1all
upstream1.5.2
Popcon: 274 users (109 upd.)*
Newer upstream!
License: DFSG free
Git

scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality:

  • Gaussian Mixture Models
  • Manifold learning
  • kNN
  • SVM (via LIBSVM)

This package contains the Python 3 version.

The package is enhanced by the following packages: python3-sklearn-pandas
Registry entries: Bio.tools  SciCrunch 
python3-statsmodels
Python3 module for the estimation of statistical models
Versions of package python3-statsmodels
ReleaseVersionArchitectures
bookworm0.13.5+dfsg-7all
stretch-backports0.8.0-9~bpo9+1all
buster0.8.0-9all
bullseye0.12.2-1all
trixie0.14.4+dfsg-1all
sid0.14.4+dfsg-1all
Popcon: 57 users (85 upd.)*
Versions and Archs
License: DFSG free
Git

statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for several distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem.

Please cite: Skipper Seabold and Josef Perktold: Statsmodels: Econometric and statistical modeling with python (eprint) (2010)
python3-thinc
Practical Machine Learning for NLP in Python
Versions of package python3-thinc
ReleaseVersionArchitectures
sid9.0.0-2amd64,arm64,armhf,i386,mips64el,riscv64,s390x
buster6.12.1-1amd64,arm64,armhf,i386
bookworm8.1.7-1amd64,arm64,armhf,i386,mips64el,s390x
Popcon: 0 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

Thinc is the machine learning library powering spaCy https://spacy.io. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0 https://spacy.io/usage/v2.

Thinc is a practical toolkit for implementing models that follow the "Embed, encode, attend, predict" architecture. It's designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

python3-torch
Tensors and Dynamic neural networks in Python (Python Interface)
Versions of package python3-torch
ReleaseVersionArchitectures
bullseye1.7.1-7amd64,arm64,armhf,ppc64el,s390x
bookworm1.13.1+dfsg-4amd64,arm64,ppc64el,s390x
sid2.4.1-1amd64,arm64,ppc64el,riscv64,s390x
Popcon: 119 users (16 upd.)*
Versions and Archs
License: DFSG free
Git

PyTorch is a Python package that provides two high-level features:

(1) Tensor computation (like NumPy) with strong GPU acceleration (2) Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed.

This is the CPU-only version of PyTorch (Python interface).

Please cite: Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai and Soumith Chintala:
Registry entries: SciCrunch 
python3-torch-sparse
PyTorch Extension Library of Optimized Autograd Sparse Matrix Operations
Versions of package python3-torch-sparse
ReleaseVersionArchitectures
sid0.6.18-2amd64,arm64,ppc64el,riscv64,s390x
Popcon: users ( upd.)*
Versions and Archs
License: DFSG free
Git

This package consists of a small extension library of optimized sparse matrix operations with autograd support.

This package installs the library for Python 3.

python3-vigra
Python3 bindings for the C++ computer vision library
Versions of package python3-vigra
ReleaseVersionArchitectures
trixie1.12.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm1.11.1+dfsg-11amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye1.11.1+dfsg-8amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid1.12.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: 9 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

Vision with Generic Algorithms (VIGRA) is a computer vision library that puts its main emphasis on flexible algorithms, because algorithms represent the principle know-how of this field. The library was consequently built using generic programming as introduced by Stepanov and Musser and exemplified in the C++ Standard Template Library. By writing a few adapters (image iterators and accessors) you can use VIGRA's algorithms on top of your data structures, within your environment.

This package exports the functionality of the VIGRA library to Python3.

r-cran-amore
GNU R: A MORE flexible neural network package
Versions of package r-cran-amore
ReleaseVersionArchitectures
jessie0.2-15-1amd64,armel,armhf,i386
buster0.2-15-3amd64,arm64,armhf,i386
bullseye0.2-16-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bookworm0.2-16-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie0.2-16-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid0.2-16-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
stretch0.2-15-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 18 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

This package was born to release the TAO robust neural network algorithm to the R users. It has grown and can be of interest for the users wanting to implement their own training algorithms as well as for those others whose needs lye only in the "user space".

r-cran-bayesm
GNU R package for Bayesian inference
Versions of package r-cran-bayesm
ReleaseVersionArchitectures
buster3.1-1-1amd64,arm64,armhf,i386
jessie2.2-5-1amd64,armel,armhf,i386
stretch3.0-2-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid3.1-6+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie3.1-6+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm3.1-5+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye3.1-4+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Debtags of package r-cran-bayesm:
fieldmathematics, statistics
suitegnu
Popcon: 21 users (4 upd.)*
Versions and Archs
License: DFSG free
Git

The bayesm package covers many important models used in marketing and micro-econometrics applications. The package includes:

  • Bayes Regression (univariate or multivariate dep var)
  • Multinomial Logit (MNL) and Multinomial Probit (MNP)
  • Multivariate Probit,
  • Multivariate Mixtures of Normals
  • Hierarchical Linear Models with normal prior and covariates
  • Hierarchical Multinomial Logits with mixture of normals prior and covariates
  • Bayesian analysis of choice-based conjoint data
  • Bayesian treatment of linear instrumental variables models
  • Analyis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)).

For further reference, consult the authors' book, Bayesian Statistics and Marketing by Allenby, McCulloch and Rossi.

r-cran-class
GNU R package for classification
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-class
ReleaseVersionArchitectures
sid7.3-22-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
jessie7.3-11-1amd64,armel,armhf,i386
stretch7.3-14-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster7.3-15-1amd64,arm64,armhf,i386
bullseye7.3-18-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bookworm7.3-21-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie7.3-22-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Debtags of package r-cran-class:
devellang:r
roleshared-lib
sciencecalculation, modelling
useanalysing
Popcon: 670 users (361 upd.)*
Versions and Archs
License: DFSG free
Git

The class package provides functions and datasets to support chapter 12 on 'Classification' in the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4

r-cran-cluster
GNU R package for cluster analysis by Rousseeuw et al
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-cluster
ReleaseVersionArchitectures
trixie2.1.6-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm2.1.4-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye2.1.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.0.7-1-1amd64,arm64,armhf,i386
stretch2.0.5-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
jessie1.15.3-1amd64,armel,armhf,i386
sid2.1.6-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Debtags of package r-cran-cluster:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 643 users (331 upd.)*
Versions and Archs
License: DFSG free
Git

This package provides functions and datasets for cluster analysis originally written by Peter Rousseeuw, Anja Struyf and Mia Hubert.

This package is part of the set of packages that are 'recommended' by R Core and shipped with upstream source releases of R itself.

r-cran-gbm
GNU R package providing Generalized Boosted Regression Models
Versions of package r-cran-gbm
ReleaseVersionArchitectures
buster2.1.5-1amd64,arm64,armhf,i386
jessie2.1-1amd64,armel,armhf,i386
sid2.2.2-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm2.1.8.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch2.1.1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye2.1.8-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 57 users (41 upd.)*
Versions and Archs
License: DFSG free
Git

This package implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

r-cran-mass
GNU R package of Venables and Ripley's MASS
Maintainer: Dirk Eddelbuettel
Versions of package r-cran-mass
ReleaseVersionArchitectures
bookworm7.3-58.2-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid7.3-61-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
jessie7.3-34-1amd64,armel,armhf,i386
stretch7.3-45-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
buster7.3-51.1-1amd64,arm64,armhf,i386
bullseye7.3-53.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie7.3-61-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Debtags of package r-cran-mass:
devellang:r
fieldstatistics
suitegnu
Popcon: 756 users (378 upd.)*
Versions and Archs
License: DFSG free
Git

The MASS package provides functions and datasets to support the book 'Modern Applied Statistics with S' (4th edition) by W.N. Venables and B.D. Ripley. The following URL provides more details about the book: URL: http://www.stats.ox.ac.uk/pub/MASS4

The package is enhanced by the following packages: r-cran-pscl
r-cran-mcmcpack
R routines for Markov chain Monte Carlo model estimation
Versions of package r-cran-mcmcpack
ReleaseVersionArchitectures
bookworm1.6-3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.4-4-1amd64,arm64,armhf,i386
sid1.7-0-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie1.7-0-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
jessie1.3-3-1amd64,armel,armhf,i386
bullseye1.5-0-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch1.3-8-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
upstream1.7-1
Debtags of package r-cran-mcmcpack:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 23 users (4 upd.)*
Newer upstream!
License: DFSG free
Git

This is a set of routines for GNU R that implement various statistical and econometric models using Markov chain Monte Carlo (MCMC) estimation, which allows "solving" models that would otherwise be intractable with traditional techniques, particularly problems in Bayesian statistics (where one or more "priors" are used as part of the estimation procedure, instead of an assumption of ignorance about the "true" point estimates), although MCMC can also be used to solve frequentist statistical problems with uninformative priors. MCMC techniques are also preferable over direct estimation in the presence of missing data.

Currently implemented are a number of ecological inference (EI) routines (for estimating individual-level attributes or behavior from aggregate data, such as electoral returns or census results), as well as models for traditional linear panel and cross-sectional data, some visualization routines for EI diagnostics, two item-response theory (or ideal-point estimation) models, metric, ordinal, and mixed-response factor analysis, and models for Gaussian (linear) and Poisson regression, logistic regression (or logit), and binary and ordinal-response probit models.

The suggested packages (r-cran-bayesm, -eco, and -mnp) contain additional models that may also be useful for those interested in this package.

The package is enhanced by the following packages: r-cran-mcmc r-cran-mnp
r-cran-metrics
GNU R evaluation metrics for machine learning
Versions of package r-cran-metrics
ReleaseVersionArchitectures
buster0.1.4-1all
bookworm0.1.4-2all
trixie0.1.4-2all
sid0.1.4-2all
bullseye0.1.4-2all
Popcon: 3 users (2 upd.)*
Versions and Archs
License: DFSG free
Git

An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

r-cran-mlbench
GNU R Machine Learning Benchmark Problems
Versions of package r-cran-mlbench
ReleaseVersionArchitectures
trixie2.1-5-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid2.1-5-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster2.1-1-3amd64,arm64,armhf,i386
bullseye2.1-3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
stretch2.1-1-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bookworm2.1-3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 166 users (43 upd.)*
Versions and Archs
License: DFSG free
Git

This GNU R package provices a collection of artificial and real-world machine learning benchmark problems, including, e.g., several data sets from the UCI repository.

r-cran-mlr
Machine learning in GNU R
Versions of package r-cran-mlr
ReleaseVersionArchitectures
trixie2.19.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,s390x
sid2.19.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
stretch-backports2.13-1~bpo9+1amd64
buster2.13-1amd64,arm64,armhf,i386
bullseye2.18.0+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bookworm2.19.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream2.19.2
Popcon: 32 users (13 upd.)*
Newer upstream!
License: DFSG free
Git

Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions. There is also an experimental extension for survival analysis, clustering and general, example-specific cost-sensitive learning. Generic resampling, including cross-validation, bootstrapping and subsampling. Hyperparameter tuning with modern optimization techniques, for single- and multi-objective problems. Filter and wrapper methods for feature selection. Extension of basic learners with additional operations common in machine learning, also allowing for easy nested resampling. Most operations can be parallelized.

r-cran-mnp
GNU R package for fitting multinomial probit (MNP) models
Versions of package r-cran-mnp
ReleaseVersionArchitectures
buster3.1-0-2amd64,arm64,armhf,i386
stretch2.6-4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bullseye3.1-1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie2.6-4-1amd64,armel,armhf,i386
bookworm3.1-3-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie3.1-4-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid3.1-4-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
upstream3.1-5
Debtags of package r-cran-mnp:
devellang:r, library
fieldstatistics
roleapp-data
suitegnu
Popcon: 15 users (3 upd.)*
Newer upstream!
License: DFSG free
Git

MNP is an R package that fits Bayesian Multinomial Probit (MNP) models via Markov chain Monte Carlo (MCMC). Along with the standard multinomial probit model, it can also fit models with different choice sets for each observation and complete or partial ordering of all the available alternatives. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2004).

r-cran-msm
GNU R модели маркова конечного числа состояний и скрытые марковские модели (СММ) на конечном промежутке времени
Versions of package r-cran-msm
ReleaseVersionArchitectures
stretch1.6.4-1amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
bookworm1.7-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
jessie1.4-2amd64,armel,armhf,i386
trixie1.8-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid1.8-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster1.6.6-2amd64,arm64,armhf,i386
bullseye1.6.8-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
upstream1.8.1
Debtags of package r-cran-msm:
interfacecommandline
roleprogram
Popcon: 52 users (108 upd.)*
Newer upstream!
License: DFSG free
Git

Функции для применения основных непрерывных во времени и скрытых моделей Маркова со множеством состояний к данным длительного наблюдения. Как Марковские коэффициенты перехода, так и результирующие Марковские скрытые процессы могут быть смоделированы через ковариации. Поддерживаются разнообразные схемы наблюдения, включая процессы наблюдаемые в произвольные моменты времени, полностью наблюдаемые процессы и запрещённые состояния.

Please cite: Christopher H. Jackson: Multi-State Models for Panel Data: The msm Package for R. Journal of Statistical Software 38(8):1-29 (2011)
r-cran-tgp
GNU R Bayesian treed Gaussian process models
Versions of package r-cran-tgp
ReleaseVersionArchitectures
stretch2.4-14-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
sid2.4-23-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie2.4-23-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm2.4-21-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye2.4-17-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster2.4-14-4amd64,arm64,armhf,i386
jessie2.4-9-1amd64,armel,armhf,i386
Popcon: 11 users (6 upd.)*
Versions and Archs
License: DFSG free
Git

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions.

root-system
??? missing short description for package root-system :-(
Versions of package root-system
ReleaseVersionArchitectures
jessie5.34.19+dfsg-1.2all
Debtags of package root-system:
fieldphysics
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
scilab-ann
??? missing short description for package scilab-ann :-(
Versions of package scilab-ann
ReleaseVersionArchitectures
stretch0.4.2.4-1all
jessie0.4.2.4-1all
Debtags of package scilab-ann:
devellibrary
roledevel-lib, shared-lib
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Svn
torch-core-free
Scientific Computing Framework For Luajit (Core Components)
Versions of package torch-core-free
ReleaseVersionArchitectures
buster20171127amd64,armhf
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

A summary of core features:

  • a powerful N-dimensional array
  • lots of routines for indexing, slicing, transposing, ...
  • amazing interface to C, via LuaJIT
  • linear algebra routines
  • neural network, and energy-based models
  • numeric optimization routines
  • Fast and efficient GPU support
  • Embeddable, with ports to iOS, Android and FPGA backends

The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.

This package is a metapackage, which pulls the following core and free modules for you: cwrap, paths, sys, xlua, torch7, nn, graph, nngraph, optim, sundown, dok, trepl, image.

Note that cutorch (CUDA backend for torch) and cunn (CUDA backend for neural network) are not present in this metapacakge - they will be shipped in the torch-core-contrib metapackage in the future.

toulbar2
Exact combinatorial optimization for Graphical Models
Versions of package toulbar2
ReleaseVersionArchitectures
sid1.2.1+dfsg-0.1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bookworm1.1.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye1.1.1+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster1.0.0+dfsg3-2amd64,arm64,armhf,i386
Popcon: 2 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

Toulbar2 is an exact discrete optimization tool for Graphical Models such as Cost Function Networks, Markov Random Fields, Weighted Constraint Satisfaction Problems and Bayesian Nets.

vowpal-wabbit
??? missing short description for package vowpal-wabbit :-(
Maintainer: Yaroslav Halchenko
Versions of package vowpal-wabbit
ReleaseVersionArchitectures
jessie7.3-1.1amd64,armel,armhf,i386
Debtags of package vowpal-wabbit:
interfacecommandline
roleprogram
scopeutility
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git
Screenshots of package vowpal-wabbit
weka
Machine learning algorithms for data mining tasks
Versions of package weka
ReleaseVersionArchitectures
stretch3.6.14-1all
jessie3.6.11-1all
trixie3.6.14-4all
sid3.6.14-4all
bookworm3.6.14-3all
bullseye3.6.14-2all
buster3.6.14-1all
upstream3.8.6
Debtags of package weka:
fieldstatistics
interfacecommandline, x11
roleprogram
sciencecalculation
scopeutility
useanalysing, calculating
works-withdb, text
x11application
Popcon: 16 users (16 upd.)*
Newer upstream!
License: DFSG free
Git

Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes.

Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets.

This package contains the binaries and examples.

yap
High-performance Prolog System
Maintainer: Ralf Treinen
Versions of package yap
ReleaseVersionArchitectures
sid6.2.2-6amd64,arm64,armel,armhf,i386
stretch6.2.2-6amd64,arm64,armel,armhf,i386
jessie6.2.2-2amd64,armel,armhf,i386
Debtags of package yap:
develcompiler, interpreter, lang:prolog
roleprogram
Popcon: 2 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

High-performance Prolog compiler developed at LIACC/Universidade do Porto and at COPPE Sistemas/UFRJ. The YAP Prolog engine is based in the Warren Abstract Machine, with several optimizations for better performance. YAP follows the Edinburgh tradition, and is largely compatible with the ISO-Prolog standard and with Quintus and SICStus Prolog.

YAP features a constraint solver over real numbers, and support for constraint handling rules (CHR).

Official Debian packages with lower relevance

ask
Adaptive Sampling Kit for big experimental spaces
Versions of package ask
ReleaseVersionArchitectures
buster1.1.1-3all
jessie1.0.1-2all
stretch1.1.1-1all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Adaptive Sampling Kit (ASK) is a toolkit for sampling big experimental spaces. When the space is small, the response can be measured for every point in the space. When the space is large, doing an exhaustive measurement is either not possible in terms of execution time or simply not practical. ASK tries to find good approximations of the response by sampling only a small fraction of the space. ASK features multiple active learning algorithms to prioritize the exploration of the interesting parts of the experimental space.

libdlib-dev
C++ toolkit for machine learning and computer vision - development
Versions of package libdlib-dev
ReleaseVersionArchitectures
bookworm19.24+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie19.24.6+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid19.24.6+dfsg-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
bullseye19.10-3.1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
buster19.10-3amd64,arm64,armhf,i386
stretch18.18-2amd64,arm64,armel,armhf,i386,mips,mips64el,mipsel,ppc64el,s390x
Popcon: 5 users (6 upd.)*
Versions and Archs
License: DFSG free
Git

Dlib is a general purpose cross-platform open source software library written in the C++ programming language. It now contains software components for dealing with networking, threads, graphical interfaces, complex data structures, linear algebra, statistical machine learning, image processing, data mining, XML and text parsing, numerical optimization, Bayesian networks, and numerous other tasks.

This package contains the development headers.

libdlpack-dev
Open In Memory Tensor Structure
Versions of package libdlpack-dev
ReleaseVersionArchitectures
bookworm0.6-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
bullseye0.0~git20200217.3ec0443-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie0.6-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid0.6-1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
upstream1.0rc
Popcon: 1 users (0 upd.)*
Newer upstream!
License: DFSG free
Git

DLPack is an open in-memory tensor structure to for sharing tensor among frameworks. DLPack enables

  • Easier sharing of operators between deep learning frameworks.
  • Easier wrapping of vendor level operator implementations, allowing collaboration when introducing new devices/ops.
  • Quick swapping of backend implementations, like different version of BLAS
  • For final users, this could bring more operators, and possibility of mixing usage between frameworks.

DLPack do not intend to implement of Tensor and Ops, but instead use this as common bridge to reuse tensor and ops across frameworks.

libfannj-java
FannJ a Java binding to the Fast Artificial Neural Network (FANN) C library
Versions of package libfannj-java
ReleaseVersionArchitectures
bullseye0.3-2all
bookworm0.7-1all
trixie0.7-1all
sid0.7-1all
jessie0.3-1all
buster0.3-2all
stretch0.3-1all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Use FannJ if you have an existing ANN from the FANN project (libfann2) that you would like to access from Java. There are several GUI tools that will help you create and train an ANN.

libfclib-dev
read and write problems from the Friction Contact Library (headers)
Versions of package libfclib-dev
ReleaseVersionArchitectures
bookworm3.1.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid3.1.0+dfsg-3amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster3.0.0+dfsg-2amd64,arm64,armhf,i386
bullseye3.1.0+dfsg-2amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie3.1.0+dfsg-3amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

fclib is an open source collection of Frictional Contact (FC) problems stored in a specific HDF5 format, and an open source light implementation of Input/Output functions in C Language to read and write problems.

The goal of this work is to set up a collection of 2D and 3D Frictional Contact (FC) problems in order to set up a list of benchmarks; provide a standard framework for testing available and new algorithms; and share common formulations of problems in order to exchange data.

Fclib is an open-source scientific software primarily targeted at modeling and simulating nonsmooth dynamical systems

This package includes the libfclib development headers.

libmkldnn-dev
Intel Math Kernel Library for Deep Neural Networks (dev)
Versions of package libmkldnn-dev
ReleaseVersionArchitectures
buster0.17.4-1amd64
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features.

One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal.

This package contains the header files, and symbol links to the shared object.

libmrgingham-dev
Chessboard finder for visual calibration routines
Versions of package libmrgingham-dev
ReleaseVersionArchitectures
bookworm1.22-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the development C++ libraries

libxgboost-predictor-java
Java implementation of XGBoost predictor for online prediction tasks
Versions of package libxgboost-predictor-java
ReleaseVersionArchitectures
bookworm0.3.1+dfsg-2all
trixie0.3.1+dfsg-2all
sid0.3.1+dfsg-2all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Kubernetes, Hadoop, SGE, MPI, Dask) and can solve problems beyond billions of examples.

libxsmm-dev
Library for matrix operations and deep learning primitives
Versions of package libxsmm-dev
ReleaseVersionArchitectures
bookworm1.17-2amd64
sid1.17-4amd64
trixie1.17-4amd64
Popcon: 3 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

LIBXSMM is a library targeting Intel Architecture for specialized dense and sparse matrix operations, and deep learning primitives.

This package contains the tools, static libraries and header files.

python3-hdmedians
high-dimensional medians in Python3
Versions of package python3-hdmedians
ReleaseVersionArchitectures
bookworm0.14.2-5amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
trixie0.14.2-5.1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
sid0.14.2-5.1amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
buster0.13~git20171027.8e0e9e3-1amd64,arm64,armhf,i386
bullseye0.14.1-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
Popcon: 35 users (3 upd.)*
Versions and Archs
License: DFSG free
Git

Various definitions for a high-dimensional median exist and this Python package provides a number of fast implementations of these definitions. Medians are extremely useful due to their high breakdown point (up to 50% contamination) and have a number of nice applications in machine learning, computer vision, and high-dimensional statistics.

This package currently has implementations of medoid and geometric median with support for missing data using NaN.

python3-imblearn
library providing resampling techniques
Versions of package python3-imblearn
ReleaseVersionArchitectures
trixie0.12.2-1all
sid0.12.2-1all
bookworm0.10.0-1all
bullseye0.7.0-6all
upstream0.12.4
Popcon: 2 users (1 upd.)*
Newer upstream!
License: DFSG free
Git

imbalanced-learn is a Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance.

It is compatible with scikit-learn and is part of scikit-learn-contrib projects.

python3-liac-arff
library for reading and writing ARFF files in Python
Versions of package python3-liac-arff
ReleaseVersionArchitectures
bookworm2.5.0-3all
sid2.5.0-6all
trixie2.5.0-6all
bullseye2.5.0-1all
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

The liac-arff module implements functions to read and write ARFF files in Python. It was created in the Connectionist Artificial Intelligence Laboratory (LIAC), which takes place at the Federal University of Rio Grande do Sul (UFRGS), in Brazil.

ARFF (Attribute-Relation File Format) is an file format specially created for describing datasets which are used commonly for machine learning experiments and software. This file format was created to be used in WEKA, the best representative software for machine learning automated experiments.

python3-mrgingham
Chessboard finder for visual calibration routines
Versions of package python3-mrgingham
ReleaseVersionArchitectures
bookworm1.22-1amd64,arm64,armel,armhf,i386,mips64el,mipsel,ppc64el,s390x
sid1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
trixie1.24-2amd64,arm64,armel,armhf,i386,mips64el,ppc64el,riscv64,s390x
Popcon: users ( upd.)*
Versions and Archs
License: DFSG free
Git

Given an observed image containing a chessboard or a grid of circles, mrgingham locates the board in the image, and precisely computes the location of the chessboard corners (or circle centers). This is similar to the routines in OpenCV, but is faster and more robust.

This package provides the Python interfaces

science-numericalcomputation
программы для выполнения числовых вычислений (из Debian Science)
Versions of package science-numericalcomputation
ReleaseVersionArchitectures
jessie1.4all
bullseye1.14.2all
bookworm1.14.5all
buster1.10all
sid1.14.6all
trixie1.14.6all
stretch1.7all
Debtags of package science-numericalcomputation:
devellang:lisp
rolemetapackage, shared-lib
Popcon: 3 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

Этот метапакет установит пакеты Debian Science, позволяющие выполнять численные расчёты. Пакеты предоставляют массиво-ориентированную систему вычислений и визуализации для научных вычислений и анализа данных. Пакеты схожи с коммерческими системами, такими как Matlab и IDL.

science-statistics
Debian Science Statistics packages
Versions of package science-statistics
ReleaseVersionArchitectures
bullseye1.14.2all
trixie1.14.6all
jessie1.4all
sid1.14.6all
bookworm1.14.5all
stretch1.7all
buster1.10all
Debtags of package science-statistics:
rolemetapackage
suitedebian
Popcon: 3 users (1 upd.)*
Versions and Archs
License: DFSG free
Git

This metapackage is part of the Debian Pure Blend "Debian Science" and installs packages related to statistics. This task is a general task which might be useful for any scientific work. It depends from a lot of R packages as well as from other tools which are useful to do statistics. Moreover the Science Mathematics task is suggested to optionally install all mathematics related software.

science-typesetting
Debian Science typesetting packages
Versions of package science-typesetting
ReleaseVersionArchitectures
bullseye1.14.2all
bookworm1.14.5all
sid1.14.6all
stretch1.7all
buster1.10all
trixie1.14.6all
jessie1.4all
Debtags of package science-typesetting:
rolemetapackage
suitedebian
Popcon: 1 users (0 upd.)*
Versions and Archs
License: DFSG free
Git

This metapackage will install Debian Science packages related to typesetting. You might also be interested in the use::typesetting debtag.

Debian packages in contrib or non-free

caffe-cuda
Fast, open framework for Deep Learning (Meta)
Versions of package caffe-cuda
ReleaseVersionArchitectures
stretch1.0.0~rc4-1 (contrib)amd64
buster1.0.0+git20180821.99bd997-2 (contrib)amd64
Popcon: 0 users (0 upd.)*
Versions and Archs
License: DFSG free, but needs non-free components
Git

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley AI Research Lab (BAIR) and community contributors.

This metapackage pulls CUDA version of caffe:

  • caffe-tools-cuda
  • libcaffe-cuda*
  • python3-caffe-cuda And suggests these packages:

  • libcaffe-cuda-dev

  • caffe-doc

Note, this CUDA version cannot co-exist with the CPU_ONLY version.

Please cite: Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama and Trevor Darrell: Caffe: Convolutional Architecture for Fast Feature Embedding. (eprint) arXiv preprint arXiv:1408.5093 (2014)

Packaging has started and developers might try the packaging code in VCS

spacy
Industrial-strength Natural Language Processing (NLP)
Versions of package spacy
ReleaseVersionArchitectures
VCS2.2.3-1all
Versions and Archs
License: MIT
Debian package not available
Git
Version: 2.2.3-1

spaCy is a library for advanced Natural Language Processing in Python and Cython. It’s built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 30+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration.

streamlit
fast way to build custom ML tools
Versions of package streamlit
ReleaseVersionArchitectures
VCS0.56.0-1all
Versions and Archs
License: Apache-2.0
Debian package not available
Git
Version: 0.56.0-1

Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Remark of Debian Science team: Needed for chime which is COVID-19 relevant.

Help for packaging is needed.

Unofficial packages built by somebody else

python3-orange
Data mining framework
Responsible: Mitar
License: GPLv3

Orange is a component-based data mining software. It includes a range of data visualization, exploration, preprocessing and modeling techniques. It can be used through a nice and intuitive user interface or, for more advanced users, as a module for Python programming language.

No known packages available but some record of interest (WNPP bug)

flann - wnpp
Fast Library for Approximate Nearest Neighbors
License: BSD
Debian package not available
Language: C++

FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search and a system for automatically choosing the best algorithm and optimum parameters depending on the dataset.

pybrain - wnpp
Modular Machine Learning Library
License: BSD
Debian package not available
Language: Python

PyBrain is a modular machine learning library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms.

PyBrain currently features algorithms for Supervised Learning, Unsupervised Learning, Reinforcment Learning and Black-box Optimization.

*Popularitycontest results: number of people who use this package regularly (number of people who upgraded this package recently) out of 244547