Description Usage Arguments Value Author(s) References Examples

This computes a covariance matrix from a squared-distance matrix using the centering method of Gower (1996). When the squared-distance matrix is a resistance distance matrix, or a variogram matrix from a spatial model, the resulting covariance matrix is the spatial covariance matrix corresponding to a random walk model for connectivity as in Hanks and Hooten (2013).

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`R` |
A negative semi-definite matrix of squared differences. |

A positive semi-definite covariance matrix, for which the variogram (or resistance distance) is equal to the input R matrix.

Ephraim M. Hanks

Hanks and Hooten 2013. Circuit theory and model-based inference for landscape connectivity. Journal of the American Statistical Association. 108(501), 22-33.

Gower 1996. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53(3), 325-338.

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```
Loading required package: raster
Loading required package: sp
Loading required package: Matrix
Loading required package: mvtnorm
Loading required package: MASS
Attaching package: 'MASS'
The following objects are masked from 'package:raster':
area, select
[1] 4.907304
```

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