Gene sets are fundamental for gene enrichment analysis. The package 'geneset' enables querying 
    gene sets from public databases including 'GO' (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), 
    'KEGG' (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), 
    'WikiPathway' (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), 
    'MsigDb' (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), 
    'Reactome' (David et al. (2011) <doi:10.1093/nar/gkq1018>),
    'MeSH' (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), 
    'DisGeNET' (Janet et al. (2017) <doi:10.1093/nar/gkw943>),
    'Disease Ontology' (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), 
    'Network of Cancer Genes' (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and 
    'COVID-19' (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>).
    Gene sets are stored in the list object which provides data frame of 'geneset' and 'geneset_name'. 
    The 'geneset' has two columns of term ID and gene ID. The 'geneset_name' has two columns of 
    terms ID and term description.

Command-line and 'shiny' GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

Access data on plant genetic resources from genebanks around the world published on Genesys (<https://www.genesys-pgr.org>).
  Your use of data is subject to terms and conditions available at <https://www.genesys-pgr.org/content/legal/terms>.

Data from multi environment agronomic trials, which are often 
  carried out by plant breeders, can be analyzed with the tools offered by this 
  package such as the Additive Main effects and Multiplicative Interaction model 
  or 'AMMI' ('Gauch' 1992, ISBN:9780444892409) 
  and the Site Regression model or 'SREG' ('Cornelius' 1996, 
  <doi:10.1201/9780367802226>). Since these methods present a poor performance 
  under the presence of outliers and missing values, this package includes 
  robust versions of the 'AMMI' model ('Rodrigues' 2016, 
  <doi:10.1093/bioinformatics/btv533>), and also imputation techniques 
  specifically developed for this kind of data ('Arciniegas-Alarcón' 2014, 
  <doi:10.2478/bile-2014-0006>).