for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(),
Imports: Rcpp, foreach, doParallel, ashr, corrplot, lpSolve, abind,

Title: Linear Dynamical System Reconstruction
person("Hung", "Nguyen", email = "ntthung@gmail.com", role = c("aut", "cre")),
person("Stefano", "Galelli", email = "stefano_galelli@sutd.edu.sg", role = "ctb")
Description: Streamflow (and climate) reconstruction using Linear Dynamical Systems.
The advantage of this method is the additional state trajectory which can reveal more information
about the catchment or climate system. For details of the method please refer to Nguyen and Galelli
Imports: data.table, foreach, MASS, Rcpp, stats
Suggests: GA, knitr, rmarkdown, testthat, ggplot2, patchwork, doFuture,
Packaged: 2020-04-29 15:58:48 UTC; The Water Guy

Title: Linear Dependence Statistics for High-Dimensional Data
to the estimation and testing of multiple correlation, partial correlation and regression coefficient matrices when data is high-dimensional.
Imports: RBGL, cluster, camel, igraph, VGAM, evd, MASS, Matrix,
Packaged: 2016-08-18 11:49:01 UTC; adriacaballe

Packaged: 2017-08-14 08:48:59 UTC; adriacaballe

Title: The Artificial Neural Networks Algorithm Leabra
person("Johannes", "Titz", email = "johannes.titz@gmail.com",
person("Sergio", "Verduczo-Flores", role = "cph"),
person("Randall", "O'Reilly", role = "cph"))
Description: The algorithm Leabra (local error driven and associative
biologically realistic algorithm) allows for the construction of artificial
neural networks that are biologically realistic and balance supervised and
unsupervised learning within a single framework. This package is based on
the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on
the description of the algorithm by Randall O'Reilly (1996)
more general (not 'R' specific) information on the algorithm Leabra see
Collate: 'data.R' 'misc.R' 'unit.R' 'layer.R' 'network.R' 'leabRa.R'

Description: The leader clustering algorithm provides
a means for clustering a set of data points. Unlike many other clustering
algorithms it does not require the user to specify the number of clusters,
but instead requires the approximate radius of a cluster as its primary
tuning parameter. The package provides a fast implementation of this
algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
and infinity) as well as for spatial data using the Haversine
formula, which takes latitude/longitude pairs as inputs and clusters