Showing 9 of total 9 results (show query)
topepo
caret:Classification and Regression Training
Misc functions for training and plotting classification and regression models.
Maintained by Max Kuhn. Last updated 4 months ago.
1.6k stars 19.24 score 61k scripts 303 dependents
jackstat
ModelMetrics:Rapid Calculation of Model Metrics
Collection of metrics for evaluating models written in C++ using 'Rcpp'. Popular metrics include area under the curve, log loss, root mean square error, etc.
Maintained by Tyler Hunt. Last updated 4 years ago.
aucloglossmachine-learningmetricsmodel-evaluationmodel-metricscpp
29 stars 11.83 score 1.3k scripts 306 dependents
cbergmeir
RSNNS:Neural Networks 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, all of the algorithmic 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.
Maintained by Christoph Bergmeir. Last updated 1 years ago.
26 stars 8.90 score 426 scripts 9 dependents
r-forge
modEvA:Model Evaluation and Analysis
Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) <doi:10.1111/ddi.12100>.
Maintained by A. Marcia Barbosa. Last updated 10 days ago.
6.83 score 269 scripts 3 dependents
philips-software
latrend:A Framework for Clustering Longitudinal Data
A framework for clustering longitudinal datasets in a standardized way. The package provides an interface to existing R packages for clustering longitudinal univariate trajectories, facilitating reproducible and transparent analyses. Additionally, standard tools are provided to support cluster analyses, including repeated estimation, model validation, and model assessment. The interface enables users to compare results between methods, and to implement and evaluate new methods with ease. The 'akmedoids' package is available from <https://github.com/MAnalytics/akmedoids>.
Maintained by Niek Den Teuling. Last updated 3 months ago.
cluster-analysisclustering-evaluationclustering-methodsdata-sciencelongitudinal-clusteringlongitudinal-datamixture-modelstime-series-analysis
30 stars 6.77 score 26 scripts
swfsc
rfPermute:Estimate Permutation p-Values for Random Forest Importance Metrics
Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for 'randomForest' results.
Maintained by Eric Archer. Last updated 4 days ago.
27 stars 5.44 score
bioc
a4Core:Automated Affymetrix Array Analysis Core Package
Utility functions for the Automated Affymetrix Array Analysis set of packages.
Maintained by Laure Cougnaud. Last updated 5 months ago.
4.38 score 2 scripts 4 dependents
metabocomp
MUVR2:Multivariate Methods with Unbiased Variable Selection
Predictive multivariate modelling for metabolomics. Types: Classification and regression. Methods: Partial Least Squares, Random Forest ans Elastic Net Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) Variable selection: Performed internally, through tuning in the inner cross-validation loop.
Maintained by Yingxiao Yan. Last updated 6 months ago.
2 stars 4.04 score 1 scripts
cran
crossval:Generic Functions for Cross Validation
Contains generic functions for performing cross validation and for computing diagnostic errors.
Maintained by Korbinian Strimmer. Last updated 2 years ago.
1.95 score 1 dependents