Editing Principal component analysis (section)

== Software/source code ==
* [[ALGLIB]] – a C++ and C# library that implements PCA and truncated PCA
* [[Analytica (software)|Analytica]] – The built-in EigenDecomp function computes principal components.
* [[ELKI]] – includes PCA for projection, including robust variants of PCA, as well as PCA-based [[Cluster analysis|clustering algorithms]].
* [[Gretl]] – principal component analysis can be performed either via the <code>pca</code> command or via the <code>princomp()</code> function.
* [[Julia language|Julia]] – Supports PCA with the <code>pca</code> function in the MultivariateStats package
* [[KNIME]] – A java based nodal arranging software for Analysis, in this the nodes called PCA, PCA compute, PCA Apply, PCA inverse make it easily.
* [[Maple (software)]] – The PCA command is used to perform a principal component analysis on a set of data.
* [[Mathematica]] – Implements principal component analysis with the PrincipalComponents command using both covariance and correlation methods.
* [https://github.com/markrogoyski/math-php MathPHP] – [[PHP]] mathematics library with support for PCA.
* [[MATLAB]] – The SVD function is part of the basic system. In the Statistics Toolbox, the functions <code>princomp</code> and <code>pca</code> (R2012b) give the principal components, while the function <code>pcares</code> gives the residuals and reconstructed matrix for a low-rank PCA approximation.
* [[Matplotlib]] – [[Python (programming language)|Python]] library have a PCA package in the .mlab module.
* [[mlpack]] – Provides an implementation of principal component analysis in [[C++]].
* [https://github.com/mikerabat/mrmath mrmath] – A high performance math library for [[Delphi (software)|Delphi]] and [[Free Pascal|FreePascal]] can perform PCA; including robust variants.
* [[NAG Numerical Library|NAG Library]] – Principal components analysis is implemented via the <code>g03aa</code> routine (available in both the Fortran versions of the Library).
* [[NMath]] – Proprietary numerical library containing PCA for the [[.NET Framework]].
* [[GNU Octave]] – Free software computational environment mostly compatible with MATLAB, the function <code>princomp</code> gives the principal component.
* [[OpenCV]]
* [[Oracle Database]] 12c – Implemented via <code>DBMS_DATA_MINING.SVDS_SCORING_MODE</code> by specifying setting value <code>SVDS_SCORING_PCA</code>
* [[Orange (software)]] – Integrates PCA in its visual programming environment. PCA displays a scree plot (degree of explained variance) where user can interactively select the number of principal components.
* [[Origin (data analysis software)|Origin]] – Contains PCA in its Pro version.
* [[Qlucore]] – Commercial software for analyzing multivariate data with instant response using PCA.
* [[R (programming language)|R]] – [[free software|Free]] statistical package, the functions <code>princomp</code> and <code>prcomp</code> can be used for principal component analysis; <code>prcomp</code> uses [[singular value decomposition]] which generally gives better numerical accuracy. Some packages that implement PCA in R, include, but are not limited to: <code>ade4</code>, <code>vegan</code>, <code>ExPosition</code>, <code>dimRed</code>, and <code>FactoMineR</code>.
* [[SAS (software)|SAS]] – Proprietary software; for example, see<ref>{{cite web|title=Principal Components Analysis|url=https://stats.idre.ucla.edu/sas/output/principal-components-analysis/|website=Institute for Digital Research and Education|publisher=UCLA|access-date=29 May 2018}}</ref>
* [[scikit-learn]] – Python library for machine learning which contains PCA, Probabilistic PCA, Kernel PCA, Sparse PCA and other techniques in the decomposition module.
* [[Scilab]] – Free and open-source, cross-platform numerical computational package, the function <code>princomp</code> computes principal component analysis, the function <code>pca</code> computes principal component analysis with standardized variables.
* [[SPSS]] – Proprietary software most commonly used by social scientists for PCA, factor analysis and associated cluster analysis.
* [[Weka (machine learning)|Weka]] – Java library for machine learning which contains modules for computing principal components.