Perform large scale genomic data retrieval and functional annotation retrieval. This package aims to provide users with a standardized
                way to automate genome, proteome, 'RNA', coding sequence ('CDS'), 'GFF', and metagenome
                retrieval from 'NCBI RefSeq', 'NCBI Genbank', 'ENSEMBL', 
                and 'UniProt' databases. Furthermore, an interface to the 'BioMart' database
                (Smedley et al. (2009) <doi:10.1186/1471-2164-10-22>) allows users to retrieve
                functional annotation for genomic loci. In addition, users can download entire databases such
                as 'NCBI RefSeq' (Pruitt et al. (2007) <doi:10.1093/nar/gkl842>), 'NCBI nr',
                'NCBI nt', 'NCBI Genbank' (Benson et al. (2013) <doi:10.1093/nar/gks1195>), etc. with only one command.

Contains functions to estimate aboveground biomass/carbon and its uncertainty in tropical forests. 
	These functions allow to (1) retrieve and to correct taxonomy, (2) estimate wood density and its uncertainty, 
	(3) construct height-diameter models, (4) manage tree and plot coordinates, 
	(5) estimate the aboveground biomass/carbon at the stand level with associated uncertainty. 
	To cite 'BIOMASS', please use citation("BIOMASS"). 
	See more in the article of Réjou-Méchain et al. (2017) <doi:10.1111/2041-210X.12753>.

Provides functions to aid in the design and analysis of
    agronomic and agricultural experiments through easy access to
    documentation and helper functions, especially for users who are
    learning these concepts. While not required for most functionality,
    this package enhances the `asreml` package which provides a 
    computationally efficient algorithm for fitting mixed models 
    using Residual Maximum Likelihood. It is a commercial package 
    that can be purchased as 'asreml-R' from 'VSNi' 
    <https://vsni.co.uk/>, who will supply a zip file for local 
    installation/updating (see <https://asreml.kb.vsni.co.uk/>).

Functions for species distribution modeling, calibration and evaluation, 
  ensemble of models, ensemble forecasting and visualization. The package permits to run
  consistently up to 10 single models on a presence/absences (resp presences/pseudo-absences)
  dataset and to combine them in ensemble models and ensemble projections. Some bench of other 
  evaluation and visualisation tools are also available within the package.