This package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

Get Real-Time Meteorological Data in SMET Format
It manages snow and weather local time series as provided by MeteoIO (<http://models.slf.ch/docserver/meteoio/html/index.html>, <http://www.slf.ch/ueber/organisation/schnee_permafrost/projekte/MeteoIO/index_EN>, <https://www.openhub.net/p/MeteoIO>). MeteoIO is a C/C++ Open Source library which "has been designed to accomodate both the needs of carefully crafted simulations for a specific purpose/study and for the needs of operational simulations that run automatically and unattended". It is integrated in physical spatially-distributed models and tackles several issues with weather input/output data. Here a SMET S4 class object is defined and can be imported from/ exported to a SMET ini files of MeteoIO , allowing interoperability from R to MeteoIO and other SMET-compliant software.

It manages snow and weather local time series as provided by
MeteoIO (<http://models.slf.ch/docserver/meteoio/html/index.html>, <http://
www.slf.ch/ueber/organisation/schnee_permafrost/projekte/MeteoIO/index_EN>,
<https://www.openhub.net/p/MeteoIO>). MeteoIO is a C/C++ Open Source library
which has been designed to settle both the needs of carefully crafted
simulations for a specific purpose/study and for the needs of operational
simulations that run automatically and unattended". It is integrated in physical
spatially-distributed models and tackles several issues with weather input/
output data. Here a SMET S4 class object is defined and can be imported from/
exported to a SMET ini files of MeteoIO , allowing interoperability from R to
MeteoIO and other SMET-compliant software.

Neural Networks in R using the Stuttgart Neural Network
Simulator (SNNS)
The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks.
This package wraps the SNNS functionality to make it available
from within R. Using the RSNNS low-level interface, nearly all
of the functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies
and learning algorithms integrate seamlessly into R.
'art1.R' 'art2.R' 'assoz.R' 'dlvq.R' 'docData.R' 'elman.R'
'jordan.R' 'mlp.R' 'parser.R' 'rbf.R' 'rbfDDA.R' 'reg_class.R'
'RSNNS-package.R' 'rsnns.R' 'SnnsDefines.R'
'SnnsR_createNets.R' 'SnnsR_masked.R' 'SnnsR_parser.R'
'SnnsR_patterns.R' 'SnnsR_train.R' 'SnnsR_util.R'
'SnnsRObjectFactory.R' 'som.R' 'util.R'