Software/source code ALGLIB – a C++ and C# library that implements PCA and truncated PCA Analytica – The built-in EigenDecomp function computes principal components. ELKI – includes PCA for projection, including robust variants of PCA, as well as PCA-based clustering algorithms. Gretl – principal component analysis can be performed either via the pca command or via the princomp() function. Julia – Supports PCA with the pca 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. MathPHP – PHP mathematics library with support for PCA. MATLAB – The SVD function is part of the basic system. In the Statistics Toolbox, the functions princomp and pca (R2012b) give the principal components, while the function pcares gives the residuals and reconstructed matrix for a low-rank PCA approximation. Matplotlib – Python library have a PCA package in the .mlab module. mlpack – Provides an implementation of principal component analysis in C++. mrmath – A high performance math library for Delphi and FreePascal can perform PCA; including robust variants. NAG Library – Principal components analysis is implemented via the g03aa 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 princomp gives the principal component. OpenCV Oracle Database 12c – Implemented via DBMS_DATA_MINING.SVDS_SCORING_MODE by specifying setting value SVDS_SCORING_PCA 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 – Contains PCA in its Pro version. Qlucore – Commercial software for analyzing multivariate data with instant response using PCA. R – Free statistical package, the functions princomp and prcomp can be used for principal component analysis; prcomp uses singular value decomposition which generally gives better numerical accuracy. Some packages that implement PCA in R, include, but are not limited to: ade4, vegan, ExPosition, dimRed, and FactoMineR. SAS – Proprietary software; for example, see 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 princomp computes principal component analysis, the function pca 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 – Java library for machine learning which contains modules for computing principal components.
software/source code alglib – a c++ and c# library that implements pca and truncated pca analytica – the built-in eigendecomp function computes principal components elki – includes pca for projection including robust variants of pca as well as pca-based clustering algorithms gretl – principal component analysis can be performed either via the pca command or via the princomp() function julia – supports pca with the pca 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 mathphp – php mathematics library with support for pca matlab – the svd function is part of the basic system in the statistics toolbox the functions princomp and pca (r2012b) give the principal components while the function pcares gives the residuals and reconstructed matrix for a low-rank pca approximation matplotlib – python library have a pca package in the mlab module mlpack – provides an implementation of principal component analysis in c++ mrmath – a high performance math library for delphi and freepascal can perform pca; including robust variants nag library – principal components analysis is implemented via the g03aa 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 princomp gives the principal component opencv oracle database 12c – implemented via dbms_data_miningsvds_scoring_mode by specifying setting value svds_scoring_pca 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 – contains pca in its pro version qlucore – commercial software for analyzing multivariate data with instant response using pca r – free statistical package the functions princomp and prcomp can be used for principal component analysis; prcomp uses singular value decomposition which generally gives better numerical accuracy some packages that implement pca in r include but are not limited to ade4 vegan exposition dimred and factominer sas – proprietary software; for example see 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 princomp computes principal component analysis the function pca 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 – java library for machine learning which contains modules for computing principal components