difference between PCA and DCA is that DCA additionally requires the input of a vector direction, referred to as the impact. Whereas PCA maximises explained variance, DCA maximises probability density given impact. The motivation for DCA is to find components of a multivariate dataset that are both likely (measured using probability density) and important (measured using the impact). DCA has been used to find the most likely and most serious heat-wave patterns in weather prediction ensembles , and the most likely and most impactful changes in rainfall due to climate change . 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