Primary function for calculating correlation ensembles

```
corEns(
time.1,
values.1,
time.2,
values.2,
bin.vec = NA,
bin.step = NA,
bin.fun = mean,
max.ens = NA,
percentiles = c(0.025, 0.25, 0.5, 0.75, 0.975),
min.obs = 10,
fdr.qlevel = 0.05,
gaussianize = TRUE,
...
)
```

- time.1
matrix of age/time ensembles, or single column

- values.1
matrix of values ensembles, or single column

- time.2
matrix of age/time ensembles, or single column

- values.2
matrix of values ensembles, or single column

- bin.vec
vector of bin edges for binning step

- bin.step
spacing of bins, used to build bin step

- bin.fun
function to use during binning (mean, sd, and sum all work)

- max.ens
Maximum number of ensembles to use

- percentiles
quantiles to calculate for regression parameters

- min.obs
minimum number of points required to calculate regression

- fdr.qlevel
target false discovery rate (most users won't want to change this)

- gaussianize
Convert data to Gaussian distribution before correlating?

- ...
Arguments passed on to

`corMatrix`

`ens.1`

matrix of age-uncertain columns to correlate and calculate p-values

`ens.2`

matrix of age-uncertain columns to correlate and calculate p-values

`isospectral`

estimate significance using the Ebisuzaki method (default = TRUE)

`isopersistent`

estimate significance using the isopersistence method (default = FALSE)

`p.ens`

number of ensemble members to use for isospectral and/or isopersistent methods (default = 100)

`cor.method`

correlation method to pass to cor() "pearson" (default), "kendall", or "spearman". Note that because the standard Student's T-test for significance is inappropriate for Kendall's Tau correlations, the raw and effective-N significance estimates will be NA when using "kendall"

list of ensemble output and percentile information