Provides the mean to parse and render markdown text with grid
    along with facilities to define the styling of the text.

Provides a single function plotting Marradi's trees: a graphical representation
 of a numerical variable for comparing the variable mean and standard deviation across subgroups. See A. Marradi "L'analisi monovariata" (1993, ISBN: 9788820496876).

Includes functions for conducting univariate and multivariate meta-analysis.
   This includes the estimation of the asymptotic variance-covariance matrix of effect sizes.
   For more details see Becker (1992) <doi:10.2307/1165128>, 
   Cooper, Hedges, and Valentine (2019) <doi:10.7758/9781610448864>, and
   Schmid, Stijnen, and White (2020) <doi:10.1201/9781315119403>.

Multivariate Adaptive Regression Spline (MARS) based Artificial Neural Network (ANN) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits ANN on the extracted important variables.

It is a hybrid spatial model that combines the strength of two widely used regression models, MARS (Multivariate Adaptive Regression Splines) and
             GWR (Geographically Weighted Regression) to provide an effective approach for predicting a response variable at unknown locations. The MARS model
             is used in the first step of the development of a hybrid model to identify the most important predictor variables that assist in predicting the response
             variable. For method details see, Friedman, J.H. (1991). <DOI:10.1214/aos/1176347963>.The GWR model is then used to predict the response variable at 
             testing locations based on these selected variables that account for spatial variations in the relationships between the variables. This hybrid model 
             can improve the accuracy of the predictions compared to using an individual model alone.This developed hybrid spatial model can be useful particularly in 
             cases where the relationship between the response variable and predictor variables is complex and non-linear, and varies across locations.