Packaged: 2022-08-22 22:07:34 UTC; luchinsky

Packaged: 2022-10-17 13:58:07 UTC; luchinsky

Packaged: 2022-10-31 14:40:47 UTC; luchinsky

Title: Gene Network Inference from Time-Series Transcriptomic Data
Description: The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.

Title: Time-Domain Deconvolution of Seismometer Response
Description: Deconvolution of instrument responses from seismic traces
and seismogram lists from RSEIS.  Includes pre-calculated
instrument responses for several common instruments.
Depends: R (>= 2.10), signal, RSEIS, pracma

Description: Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.

Description: Tuned Data Mining in R (TDMR) performs a complete tuning of a data mining task.
Depends: R (>= 1.8.0), SPOT, randomForest, e1071, snowfall, matlab

Depends: R (>= 1.8.0), SPOT, randomForest, e1071, snowfall

Description: Tuned Data Mining in R (TDMR) performs a complete tuning
Depends: R (>= 1.8.0), SPOT, randomForest, e1071, snowfall, cmaes,

Depends: R (>= 2.14.0), SPOT, randomForest, e1071, snowfall, cmaes,

Depends: R (>= 2.14.0), SPOT, cmaes, e1071, powell, randomForest, rSFA,

Author: Wolfgang Konen <wolfgang.konen@fh-koeln.de>, Patrick Koch

Description: Tuned Data Mining in R (TDMR) performs a complete tuning of a data
Suggests: adabag, cmaes, parallel, powell, ROCR, twiddler,

Suggests: adabag, cmaes, parallel, e1071, powell, ROCR, randomForest,

Suggests: cmaes, parallel, e1071, powell, ROCR, randomForest, rCMA,

Description: Tuned Data Mining in R ('TDMR') performs the complete tuning of a
data mining task (predictive analytics, that is classification and regression).
Preprocessing parameters and modeling parameters can be tuned simultaneously.
It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and
allows integration of additional tuners. Noise handling in the data mining optimization
process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.
Depends: R (>= 2.14.0), SPOT (>= 2.0), twiddler