Packaged: 2016-02-02 02:45:13 UTC; giridhargopalan

Title: Fast and Exact Computation of Gaussian Stochastic Process
Description: Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu (2017) <arXiv:1711.11501>.
Packaged: 2019-01-03 16:10:03 UTC; gumengyang

Packaged: 2019-04-12 14:27:00 UTC; gumengyang

Description: Implements fast and exact computation of Gaussian stochastic process with the Matern kernel using forward filtering and backward smoothing algorithm. It allows for the cases with or without a noise.  See the reference: Mengyang Gu and Yanxun Xu (2017),  <arXiv:1711.11501>.
Packaged: 2021-08-29 04:38:10 UTC; mengyanggu

Title: R package to compute the FastHCS outlyingness index.
Description: The FastHCS outlyingness index is a method for finding outliers in high dimensional data-sets.
Authors@R: person("Kaveh", "Vakili", email =
"kaveh.vakili@wis.kuleuven.be", role = c("aut", "cre"))

Title: FastHCS Robust Algortihm for Principal Component Analysis
Description: The FastHCS algorithm of Schmitt and Vakili (2014) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
Authors@R: person("Kaveh", "Vakili", email =  "vakili.kaveh.email@gmail.com", role = c("aut", "cre"))

Depends: R (>= 3.1.1), matrixStats, rrcov

Title: Robust Algorithm for Principal Component Analysis
Depends: R (>= 3.1.1), matrixStats, robustbase

Title: FastHCS Robust Algorithm for Principal Component Analysis
Description: The FastHCS algorithm of Schmitt and Vakili (2014) <doi:10.1007/s11222-015-9602-5> for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.

Description: The FastHCS algorithm of Schmitt and Vakili (2015) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.

Title: Learn from training data then quickly fill in missing data.
Maintainer: Stephen R. Haptonstahl <srh@haptonstahl.org>
Description: TrainFastImputation uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this
information as an object of class FastImputationPatterns. The
FastImputation function uses this FastImputationPatterns object
to impute (make a good guess at) missing data in a single line
or a whole dataframe of data.  This approximates the process
used by Amelia (\url{http://gking.harvard.edu/amelia/}) but is
much faster when filling in values for a single line of data.