Multivariate Analysis of Metabolomics Data using Random Forests
A collection of tools for multivariate analysis of metabolomics
data, which includes several preprocessing methods (normalization, scaling)
and various exploration and data visualization techniques (Principal
Components Analysis and Multi Dimensional Scaling). The core of the package
is the Random Forest algorithm used for the construction, optimization and
validation of classification models with the aim of identifying potentially
relevant biomarkers.
randomForest, ggplot2, UsingR, WilcoxCV, ROCR, methods

randomForest, ggplot2, UsingR, WilcoxCV, ROCR

Merging of Satellite Datasets with Ground Observations using
Random Forests
Mauricio Zambrano-Bigiarini [aut, cre, cph]
(<https://orcid.org/0000-0002-9536-643X>),
Oscar M. Baez-Villanueva [aut, cph],
Juan Giraldo-Osorio [ctb]
c(person("Mauricio Zambrano-Bigiarini", email = "mzb.devel@gmail.com", role=c("aut", "cre", "cph"), comment=c(ORCID = "0000-0002-9536-643X")), person("Oscar M. Baez-Villanueva", email = "obaezvil@th-koeln.de", role=c("aut", "cph")), person("Juan Giraldo-Osorio", email = "j.giraldoo@javeriana.edu.co", role=c("ctb")) )
S3 implementation of the Random Forest MErging Procedure (RF-MEP), which combines two or more satellite-based datasets (e.g., precipitation products, topography) with ground observations to produce a new dataset with improved spatio-temporal distribution of the target field. In particular, this package was developed to merge different Satellite-based Rainfall Estimates (SREs) with measurements from rain gauges, in order to obtain a new precipitation dataset where the time series in the rain gauges are used to correct different types of errors present in the SREs. However, this package might be used to merge other hydrological/environmental satellite fields with point observations. For details, see Baez-Villanueva et al. (2020) <doi:10.1016/j.rse.2019.111606>. Bugs / comments / questions / collaboration of any kind are very welcomed.
raster, sp, sf, randomForest, zoo, parallel, methods, stats,
utils, pbapply