The developed function is a comprehensive tool for the analysis of India Meteorological Department (IMD) NetCDF rainfall data. Specifically designed to process high-resolution daily
             gridded rainfall datasets. It provides four key functions to process IMD NetCDF rainfall data and create rasters for various temporal scales, including annual, seasonal, monthly, and weekly
             rainfall. For method details see, Malik, A. (2019).<DOI:10.1007/s12517-019-4454-5>. It supports different aggregation methods, such as sum, min, max, mean, and standard deviation. These functions
             are designed for spatio-temporal analysis of rainfall patterns, trend analysis,geostatistical modeling of rainfall variability, identifying rainfall anomalies and extreme events and can be an input
             for hydrological and agricultural models.

A robust constrained L1 minimization method for estimating a large sparse inverse covariance matrix (aka precision matrix), and recovering its support for building graphical models.  The computation uses linear programming.  The method was published in TT Cai, W Liu, X Luo (2011) <doi:10.1198/jasa.2011.tm10155>.

Provides interactive command-line menu functionality with
    single and multiple selection menus, keyboard navigation (arrow keys or
    vi-style j/k), preselection, and graceful fallback for non-interactive
    environments. Inspired by tools such as 'inquirer.js'
    <https://github.com/SBoudrias/Inquirer.js>, 'pick'
    <https://github.com/aisk/pick>, and 'survey'
    <https://github.com/AlecAivazis/survey>. Designed to be lightweight and
    easy to integrate into 'R' packages and scripts.

A framework that facilitates spatio-temporal analysis of climate dynamics through exploring and measuring different dimensions of climate change in space and time.

Functions for fitting GEV and POT (via point process fitting)
    models for extremes in climate data, providing return values, return
    probabilities, and return periods for stationary and nonstationary models.
    Also provides differences in return values and differences in log return
    probabilities for contrasts of covariate values. Functions for estimating risk
    ratios for event attribution analyses, including uncertainty. Under the hood,
    many of the functions use functions from 'extRemes', including for fitting the
    statistical models. Details are given in Paciorek, Stone, and Wehner (2018)
    <doi:10.1016/j.wace.2018.01.002>.