For QTL mapping, it consists of several functions to perform various tasks, including 
    simulating or analyzing data, computing the significance thresholds and visualizing the 
    QTL mapping results. The single-QTL or multiple-QTL method that allows a host of statistical 
    models to be fitted and compared is applied to analyze the data for the estimation of QTL 
    parameters. The models include the linear regression, permutation test, normal mixture model 
    and truncated normal mixture model. The Gaussian stochastic process is implemented to 
    compute the significance thresholds for QTL detection onto a genetic linkage map in the 
    experimental populations. Two types of data, the complete genotyping or selective genotyping 
    data, from various experimental populations, including backcross, F2, recombinant inbred (RI) 
    populations, advanced intercrossed (AI) populations, are considered in the QTL mapping 
    analysis. For QTL hotpot detection, the statistical methods can be developed based on either 
    using the individual-level data or using the summarized data. We have proposed a statistical 
    framework that can handle both the individual-level data and summarized QTL data for QTL 
    hotspots detection. Our statistical framework can overcome the underestimation of threshold 
    arising from ignoring the correlation structure among traits, and also identify the different 
    types of hotspots with very low computational cost during the detection process. Here, we 
    attempt to provide the R code for our statistical framework. The QTL mapping program is 
    carried out by EM process, which can be seen in Kao, C.-H. ,Z.-B. Zeng and R. D. Teasdale
    (1999) <doi:10.1534/genetics.103.021642>. The analyze of selective genotyping can be seen in 
    H.-I LEE, H.-A. HO  and C.-H. Kao (2014) <doi:10.1534/genetics.114.168385>. The analyze of 
    QTL hotspot detection can be seen in Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2020) 
    <doi:10.1101/2020.08.13.249342>.