Framework modules 
Accord.NET provides statistical analysis, machine learning, image processing and computer vision methods for .NET applications. The Accord.NET Framework extends the popular AForge.NET with new features, adding to a more complete environment for scientific computing in .NET.
The framework is divided in libraries, available through an executable installer, standalone compressed archives and NuGet packages. Those libraries are divided among three main functionalities, listed below:
A complete listing of the framework's namespaces is presented below. Please click on any of the namespace names for more details.
Namespace  Description 

Accord  
Accord.Audio  
Accord.Audio.ComplexFilters  Contains frequencydomain signal filters. 
Accord.Audio.Filters  Contains timedomain signal processing filters. 
Accord.Audio.Formats  
Accord.Audio.Generators  Contains specialized signal generators. Generate square signals, sinusoids, pulse and other filters for use in signal processing. 
Accord.Audio.Windows  Contains audio window functions which can be used to split signals in time. 
Accord.Audition.Beat  Contains beat detection algorithms and related methods. 
Accord.Collections  Contains collections such as Lists, Dictionaries, Trees and other useful structures. 
Accord.Controls  
Accord.Controls.Vision  
Accord.Diagnostics  
Accord.DirectSound  Contains audio devices to reproduce and capture sounds exposed through DirectSound. 
Accord.Fuzzy  
Accord.Genetic  
Accord.Imaging  
Accord.Imaging.ColorReduction  
Accord.Imaging.ComplexFilters  
Accord.Imaging.Converters 
Contains classes and methods to convert between different image representations,
such as between common images, numeric matrices and arrays.

Accord.Imaging.Filters  Contains the image processing filters such as the Wavelet transform, stereo rectification, image blending and point markers. 
Accord.Imaging.Formats  
Accord.Imaging.Moments  Contains image moments calculators such as central and raw moments, 
Accord.Imaging.Textures  
Accord.IO  
Accord.MachineLearning  
Accord.MachineLearning.Bayes  Contains discrete and continuous density Naive Bayes models for pattern recognition and concept learning. Supports a wide diversity of probabilistic distributions. 
Accord.MachineLearning.Boosting 
Contains Boosting related techniques for creating classifier ensembles and other composition models.

Accord.MachineLearning.Boosting.Learners 
Contains Boosting related techniques for creating classifier ensembles and other composition models.

Accord.MachineLearning.Clustering  
Accord.MachineLearning.DecisionTrees 
Contains discrete and continuous Decision Trees, with
support for automatic code generation, tree pruning and
the creation of decision rule sets.

Accord.MachineLearning.DecisionTrees.Learning 
Contains learning algorithms for inducing
Decision Trees.

Accord.MachineLearning.DecisionTrees.Pruning 
Contains classes to prune decision trees, removing
unneeded nodes in an attempt to improve generalization.

Accord.MachineLearning.DecisionTrees.Rules  
Accord.MachineLearning.Geometry  Contains methods for robust estimation of geometry entities. 
Accord.MachineLearning.Rules  
Accord.MachineLearning.VectorMachines 
Contains classes related to Support Vector Machines (SVMs).
Contains linear machines,
kernel machines, multiclass machines, SVMDAGs
(Directed Acyclic Graphs), multilabel classification
and also offers support for the probabilistic output calibration
of SVM outputs.

Accord.MachineLearning.VectorMachines.Learning  Contains algorithms for training Support Vector Machines (SVMs). 
Accord.Math  
Accord.Math.Comparers 
Comparison methods that can be used in sort
algorithms such as Sort(Array).

Accord.Math.Convergence  
Accord.Math.Decompositions  
Accord.Math.Differentiation  Contains methods for the automatic differentiation of mathematical formulas, such as the Finite Differences method. 
Accord.Math.Distances  
Accord.Math.Environments  Contains algorithm environments you can inherit from and let your code be similar to famous environments such as R and Octave. 
Accord.Math.Geometry  Contains geometryrelated classes. Can identify convexhulls, detect curvatures and extract convexity defects. When used together with the Imaging and Vision namespaces, can create finger detection components. 
Accord.Math.Integration 
Numerical methods for approximating integrals.

Accord.Math.Kinematics  Contains classes to model complex kinematic chains, useful for robotic applications. 
Accord.Math.Metrics  
Accord.Math.Optimization 
Contains classes for constrained and unconstrained optimization. Includes
Conjugate Gradient (CG),
Bounded and Unbounded Broyden–Fletcher–Goldfarb–Shanno (BFGS),
gradientfree optimization methods such as Cobyla and the GoldfarbIdnani
solver for Quadratic Programming (QP) problems.

Accord.Math.Optimization.Losses  
Accord.Math.Random  
Accord.Math.Transforms  
Accord.Math.Wavelets  Contains Wavelet transforms such as the CohenDaubechiesFeauveau and the Haar Wavelet transforms. 
Accord.Neuro  
Accord.Neuro.ActivationFunctions  Contains different activation functions for artificial neurons. 
Accord.Neuro.Layers  Contains different layer architecures for artificial neural networks. 
Accord.Neuro.Learning  Contains neural network learning algorithms such as the LevenbergMarquardt (LM) with Bayesian Regularization and the Resilient Backpropagation (RProp) for multilayer networks. This namespace extends the AForge.Neuro namespace of the AForge.NET project. 
Accord.Neuro.Networks  Contains different neural network architectures, such as specialized architectures for deep learning and Boltzmann machines. 
Accord.Neuro.Neurons  Contains different kinds of artificial neurons. 
Accord.Neuro.Visualization  Contains methods to visualize information drawn from neural networks. 
Accord.Statistics  
Accord.Statistics.Analysis 
Contains many statistical analysis, such as PCA,
LDA,
KPCA, KDA,
PLS, ICA,
Logistic Regression and Stepwise Logistic Regression
Analyses. Also contains performance assessment analysis such as
contingency tables and ROC curves.

Accord.Statistics.Analysis.Base  
Accord.Statistics.Analysis.ContrastFunctions  Contains contrast functions to be used with Independent Component Analysis (ICA). 
Accord.Statistics.Distances  
Accord.Statistics.Distributions 
Contains more than 40 statistical distributions, with support
for most probability distribution measures and estimation methods.

Accord.Statistics.Distributions.DensityKernels 
Contains density estimation kernels which can be used in combination
with empirical distributions
and multivariate empirical
distributions.

Accord.Statistics.Distributions.Fitting 
Contains special options which can be used in
distribution fitting (estimation) methods.

Accord.Statistics.Distributions.Multivariate 
Contains a multivariate distributions such as the
multivariate Normal, Multinomial,
Independent,
Joint and Mixture distributions.

Accord.Statistics.Distributions.Sampling  
Accord.Statistics.Distributions.Univariate 
Contains univariate distributions such as Normal,
Cauchy,
Hypergeometric, Poisson,
Bernoulli, and specialized distributions such
as the KolmogorovSmirnov,
Nakagami,
Weibull, and VonMises distributions.

Accord.Statistics.Filters  Contains data processing filters, such as data normalization, discretization, equalization, selection and projection filters. 
Accord.Statistics.Kernels  Contains more than 30+ kernel functions for machine learning and statistical applications. Kernel functions are used in kernel methods such as the Support Vector Machine (SVM). 
Accord.Statistics.Kernels.Sparse  Contains kernel function able to deal with sparse data in LibSVM's format. 
Accord.Statistics.Links  Contains link functions for generalized linear models, such as the Logit, the Probit and Cauchit link functions. 
Accord.Statistics.Models 
Contains statistical models with direct applications in machine learning, such as
Hidden Markov Models,
Conditional Random Fields, Hidden Conditional
Random Fields and linear and
logistic regressions.

Accord.Statistics.Models.Fields 
Contains classes related to Conditional Random
Fields, Hidden Conditional Random
Fields and their learning
algorithms.

Accord.Statistics.Models.Fields.Features  Contains CRF feature functions such as Emission, Transition, First and Second Moments features. 
Accord.Statistics.Models.Fields.Functions  Contains potential functions for CRFs and HCRFs. 
Accord.Statistics.Models.Fields.Functions.Specialized  
Accord.Statistics.Models.Fields.Learning 
Contains learning algorithms for CRFs and
HCRFs, such as
Conjugate Gradient,
LBFGS and
RPropbased learning.

Accord.Statistics.Models.Markov  Contains classes related to Hidden Markov Models and their learning algorithms. Offers support for both discrete and continuousdensity models, as well as Markov classifiers and threshold models for sequence rejection. 
Accord.Statistics.Models.Markov.Hybrid  
Accord.Statistics.Models.Markov.Learning  Contains learning algorithms such as BaumWelch. 
Accord.Statistics.Models.Markov.Topology  Contains topologies for HMMs, such as Forwardonly and Ergodic topologies. 
Accord.Statistics.Models.Regression  Contains statistical regression models such as logistic and linear regressions. 
Accord.Statistics.Models.Regression.Fitting  Fitting (learning) algorithms for regression models, such as the Iterative Reweighted Least Squares for standard logistic regressors and the LowerBound approximator for multinomial logistic regression. 
Accord.Statistics.Models.Regression.Linear  Linear statistical regression models such as simple, polynomial, multiple and multivariate linear regressions. 
Accord.Statistics.Moving  Contains classes to estimate moving statistics, i.e. statistics computed within a time frame window. 
Accord.Statistics.Running  Contains classes to estimate running statistics, i.e. statistics which should be computed and updated as soon as new data becomes available. 
Accord.Statistics.Testing 
Contains 34+ statistical hypothesis tests, including one way
and twoway ANOVA tests, nonparametric tests such as the
KolmogorovSmirnov test and the
Sign Test for the Median, contingency table
tests such as the Kappa test, including variations for
multiple tables, as well as the
Bhapkar and Bowker tests; and the more traditional
ChiSquare, Z, F
, T and Wald tests.

Accord.Statistics.Testing.Power 
Contains methods for power analysis of several related hypothesis tests,
including support for automatic sample size estimation.

Accord.Statistics.Visualizations  Contains classes for statistical visualization such as Histograms and Scatterplots. 
Accord.Video  
Accord.Video.DirectShow  
Accord.Video.FFMPEG  
Accord.Video.Kinect  
Accord.Video.VFW  
Accord.Video.Ximea  
Accord.Vision  
Accord.Vision.Detection  Contains object detectors such as the ViolaJones (Haar feature) method. The Haar cascades are completely compatible with OpenCV generated definitions and the assembly comes with direct support for bundled definitions for face and nose templates. 
Accord.Vision.Detection.Cascades  Builtin Haar cascade definitions to use with the Haar feature object detector. Those definitions can be called directly from code without need for loading XML files. 
Accord.Vision.Motion  
Accord.Vision.Tracking  Contains classes for object tracking. Include the Camshift algorithm, color segmentationbased trackers and dynamic template matching trackers. 