Plotting package based on the grid system, combining elements of 
    a bubble plot and heatmap to conveniently display two numerical variables, 
    (represented by color and size) grouped by categorical variables on the x 
    and y axes. This is a useful alternative to a forest plot when the data can 
    be grouped in two dimensions, such as predictors x outcomes. It has 
    particular advantages for visualising the metabolic measures produced by 
    the 'Nightingale Health' metabolomics platform, and templates are included 
    for automatically generating figures from these datasets.

Creates bubbles within 'shiny' and 'rmarkdown' backgrounds using the 'bubbly-bg' 'JavaScript' library.

Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and
    uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

Statistical classification and regression have been popular among various fields and stayed in the limelight of scientists of those fields. Examples of the fields include clinical trials where the statistical classification of patients is indispensable to predict the clinical courses of diseases. Considering the negative impact of diseases on performing daily tasks, correctly classifying patients based on the clinical information is vital in that we need to identify patients of the high-risk group to develop a severe state and arrange medical treatment for them at an opportune moment. Deep learning - a part of artificial intelligence - has gained much attention, and research on it burgeons during past decades: see, e.g, Kazemi and Mirroshandel (2018) <DOI:10.1016/j.artmed.2017.12.001>. It is a veritable technique which was originally designed for the classification, and hence, the Buddle package can provide sublime solutions to various challenging classification and regression problems encountered in the clinical trials. The Buddle package is based on the back-propagation algorithm - together with various powerful techniques such as batch normalization and dropout - which performs a multi-layer feed-forward neural network: see Krizhevsky et. al (2017) <DOI:10.1145/3065386>, Schmidhuber (2015) <DOI:10.1016/j.neunet.2014.09.003> and LeCun et al. (1998) <DOI:10.1109/5.726791> for more details. This package contains two main functions: TrainBuddle() and FetchBuddle(). TrainBuddle() builds a feed-forward neural network model and trains the model. FetchBuddle() recalls the trained model which is the output of TrainBuddle(), classifies or regresses given data, and make a final prediction for the data.