# pcaMethods R package for performing [principal component analysis PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) with applications to missing value imputation. Provides a single interface to performing PCA using - **SVD:** a fast method which is also the standard method in R but which is not applicable for data with missing values. - **NIPALS:** an iterative fast method which is applicable also to data with missing values. - **PPCA:** Probabilistic PCA which is applicable also on data with missing values. Missing value estimation is typically better than NIPALS but also slower to compute and uses more memory. A port to R of the [implementation by Jakob Verbeek](http://lear.inrialpes.fr/~verbeek/software.php). - **BPCA:** Bayesian PCA which performs very well in the presence of missing values but is slower than PPCA. A port of the [matlab implementation by Shigeyuki Oba](http://ishiilab.jp/member/oba/tools/BPCAFill.html). - **NLPCA:** Non-linear PCA which can find curves in data and in presence of such can perform accurate missing value estimation. [Matlab port of the implementation by Mathias Scholz](http://www.nlpca.org/). [pcaMethods is a Bioconductor package](http://www.bioconductor.org/packages/release/bioc/html/pcaMethods.html) and you can install it by ```R if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("pcaMethods") ``` ## Documentation ```R browseVignettes("pcaMethods") ?<function_name> ```