Ensemble PCA, or Monte Carlo Empirical Orthogonal Functions

```
pcaEns(
bin.list,
method = "ppca",
weights = NA,
pca.type = "corr",
gaussianize = TRUE,
n.pcs = 8,
n.ens = 1000,
simulateTrendInNull = FALSE
)
```

- bin.list
A list of binned data, the output of binTs()

- method
What method to use for PCA? pcaMethods::listPcaMethods() for options. "ppca" is default. Other options may not work in GeoChronR.

- weights
Vector of weights to apply to timeseries in the bin.list

- pca.type
Correlation ("corr" - default) or Covariance ("cov"), matrix

- gaussianize
Map input data to a standard Gaussian distribution? This is only relevant for correlation matrices, covariance matrices will not be gaussianized. (default = TRUE)

- n.pcs
number of PCs/EOFs to calculate

- n.ens
how many ensemble members to calculate

- simulateTrendInNull
Should the null include the trend?

Other pca:
`ar1Surrogates()`

,
`createSyntheticTimeseries()`

,
`plotPcaEns()`

,
`plotScreeEns()`