This book intends to present the basic tools to statistical programming and software development using the wide variety of tools made available through R, from method-specific packages to version control programs. The general goals of the book are therefore the following: • understand data structures in order to appropriately manage data, computer memory and computations; • manipulate data structures through controls, instructions, and tailored functions in order to achieve a desired output; • create new software tools (packages and web applications) that accommodate a previously unmet need; • learn how to manage software development via version control tools (e.g., GitHub) and create documentation for this software (with embedded code) to allow others to make use of the software.

• Author and maintainer of wavelet variance (wv) package, which uses parameter estimation techniques to calculate discrete wavelet transformation coefficients, maximum overlap discrete (stationary) wavelet transformation with additional emphasis on novel methods of spatial wavelet cases • Working on optimization of code and efficient memory management in Rcpp, for future implementation on big datasets • Arrange meetings with supervisor and effectively presented critical findings using R Shiny for visiting scholars, using version control with public GitHub repository for open-source distribution of algorithm

• Author and maintainer of wavelet variance (simts) package, which generates various time series objects from TS models and TS data objects, for use in other packages. • Generate website framework and vignettes for written guide to using the package

Classified outcomes of housing feedback with over 200 predictors using machine learning algorithms such as Random Forest, SVM, penalized logistic regression. Presented recommendations for resource reallocations specific to different geographic and demographic areas to benefit the company and the tenants financially.

• Created interactive pairwise bonding network visualization (pairbond-net) of gene pairs using regular expressions, HTML widgets and R Shiny, in which user can control tension of edges, size, font, and number of hierarchical clusters by visualizing dendrogram • Arranged meetings with supervisor and effectively presented critical findings using R Shiny for visiting scholars, using version control with public GitHub repository for open-source distribution of algorithm