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>).

An integrated set of tools to allow data users to conduct 
    meteorological normalisation and counterfactual modelling for air quality 
    data. The meteorological normalisation technique uses predictive random 
    forest models to remove variation of pollutant concentrations so trends and 
    interventions can be explored in a robust way. For examples, see 
    Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and 
    Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random
    forest models can also be used for counterfactual or business as usual (BAU) 
    modelling by using the models to predict, from the model's perspective, the 
    future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.

Extend Rasch and Item Response Theory (IRT) analyses by providing  
 tools for post-processing the output from five major IRT packages  
 (i.e., 'eRm', 'psychotools', 'ltm', 'mirt', and 'TAM').            
 The current version provides the plotPIccc() function, which       
 extracts from the return object of the originating package all     
 information required to draw an extended Person-Item-Map (PIccc),  
 showing any combination of                                         
 * category characteristic curves (CCCs),                           
 * threshold characteristic curves (TCCs),                          
 * item characteristic curves (ICCs),                               
 * category information functions (CIFs),                           
 * item information functions (IIFs),                               
 * test information function (TIF), and the                         
 * standard error curve (S.E.).                                     
 for uni- and multidimensional models (as far as supported by each  
 package). It allows for selecting dimensions, items, and categories
 to plot and offers numerous options to adapt the output. The return
 object contains all calculated values for further processing.