Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool (rMVP)
A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
(1) effectively process large data,
(2) rapidly evaluate population structure,
(3) efficiently estimate variance components several algorithms,
(4) implement parallel-accelerated association tests of markers three methods,
(5) globally efficient design on GWAS process computing,
(6) enhance visualization of related information.
‘rMVP’ contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>)
and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX
(Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>,
R implementation from ‘GAPIT2’: You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and
‘SUPER’: Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression
(Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).

Read ‘Excel’ Binary (.xlsb) Workbooks (readxlsb)
Import data from ‘Excel’ binary (.xlsb) workbooks into R.

Algebraic and Statistical Functions for Genetics (miraculix)
This is a collection of fast tools for application in quantitative genetics. For instance, the SNP matrix can be stored in a minimum of memory and the calculation of the genomic relationship matrix is based on a rapid algorithm. It also contains the window scanning approach by Kabluchko and Spodarev (2009), <doi:10.1239/aap/1240319575> to detect anomalous genomic areas <doi:10.1186/s12864-018-5009-y>. Furthermore, the package is used in the Modular Breeding Program Simulator (MoBPS, <https://…/MoBPS>, <http://…/> ). The tools are based on SIMD (Single Instruction Multiple Data, <https://…/SIMD> ) and OMP (Open Multi-Processing, <https://…/OpenMP> ).

Access Landscape Evaporative Response Index Raster Data (leri)
Finds and downloads Landscape Evaporative Response Index (LERI) data, then reads the data into ‘R’ using the ‘raster’ package. The LERI product measures anomalies in actual evapotranspiration, to support drought monitoring and early warning systems. More info on LERI is available at <https://…/>.

Boltzmann Bayes Learner (bbl)
Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood and mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. Woo et al. (2016) <doi:10.1186/s12864-016-2871-3>.