Description Details Author(s) References Examples

Fast and numerically stable estimation of covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <https://stat.umn.edu/~molst029> for details on the algorithm.

Package: | FastBandChol |

Type: | Package |

Version: | 0.1.0 |

Date: | 2015-08-22 |

License: | GPL-2 |

Aaron Molstad

Rothman, A.J., Levina, E., and Zhu, J. (2010). A new approach to Cholesky-based covariance regularization in high dimensions. Biometrika, 97(3):539-550.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## set sample size and dimension
n = 20
p = 100
## create covariance with AR1 structure
Sigma = matrix(0, nrow=p, ncol=p)
for(l in 1:p){
for(m in 1:p){
Sigma[l,m] = .5^(abs(l-m))
}
}
## simulation Normal data
eo1 = eigen(Sigma)
Sigma.sqrt = eo1$vec%*%diag(eo1$val^.5)%*%t(eo1$vec)
X = t(Sigma.sqrt%*%matrix(rnorm(n*p), nrow=p, ncol=n))
## compute estimates
est.sample = banded.sample(X, bandwidth=4)$est
est.chol = banded.chol(X, bandwidth=4)$est
``` |

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