A simple method
    to select the best model or best subset of variables using
    different types of data (binary, Gaussian or Poisson) and
    applying it in different contexts (parametric or non-parametric).

Implementation of fast algorithms for wild cluster bootstrap 
             inference developed in 'Roodman et al' (2019, 'STATA' Journal,
             <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), 
             which makes it feasible to quickly calculate bootstrap test 
             statistics based on a large number of bootstrap draws even for 
             large samples. Multiple bootstrap types as described in 'MacKinnon, 
             Nielsen & Webb' (2022) are supported. 
             Further, 'multiway' clustering, regression weights, 
             bootstrap weights, fixed effects and 'subcluster' bootstrapping
             are supported. Further, both restricted ('WCR') and unrestricted
             ('WCU') bootstrap are supported. Methods are provided for a variety 
             of fitted models, including 'lm()', 'feols()' 
             (from package 'fixest') and 'felm()' (from package 'lfe'). 
             Additionally implements a 'heteroskedasticity-robust' ('HC1') wild 
             bootstrap.
             Last, the package provides an R binding to 'WildBootTests.jl',
             which provides additional speed gains and functionality, 
             including the 'WRE' bootstrap for instrumental variable models 
             (based on models of type 'ivreg()' from package 'ivreg')
             and hypotheses with q > 1.

Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <arXiv:2307.00369>.

Fresh biomass determination is the key to evaluating crop genotypes' response to diverse input and stress conditions and forms the basis for calculating net primary production. However, as conventional phenotyping approaches for measuring fresh biomass is time-consuming, laborious and destructive, image-based phenotyping methods are being widely used now. In the image-based approach, the fresh weight of the above-ground part of the plant depends on the projected area. For determining the projected area, the visual image of the plant is converted into the grayscale image by simply averaging the Red(R), Green (G) and Blue (B) pixel values. Grayscale image is then converted into a binary image using Otsu’s thresholding method Otsu, N. (1979) <doi:10.1109/TSMC.1979.4310076> to separate plant area from the background (image segmentation). The segmentation process was accomplished by selecting the pixels with values over the threshold value belonging to the plant region and other pixels to the background region. The resulting binary image consists of white and black pixels representing the plant and background regions. Finally, the number of pixels inside the plant region was counted and converted to square centimetres (cm2) using the reference object (any object whose actual area is known previously) to get the projected area. After that, the projected area is used as input to the machine learning model (Linear Model, Artificial Neural Network, and Support Vector Regression) to determine the plant's fresh weight.