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R package for performing principal component analysis PCA 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.**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.**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.

pcaMethods is a Bioconductor package and you can install it by

```
source("https://bioconductor.org/biocLite.R")
biocLite("pcaMethods")
```

```
browseVignettes("pcaMethods")
?<function_name>
```

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