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
[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
if (!requireNamespace("BiocManager", quietly=TRUE))