Functions for generating variants of curves:
    restricted cubic spline, periodic restricted cubic spline,
    periodic cubic spline. Periodic splines can be used to model data
    that has periodic nature / seasonality.

Provides a dataset containing properties for chemical elements.
    Helper functions are also provided to access some atomic properties.

An enterprise-targeted scalable and UI-standardized 'shiny' framework 
    including a variety of developer convenience functions with the goal of both 
    streamlining robust application development while assisting with creating a 
    consistent user experience regardless of application or developer.

A framework for building enterprise, scalable and UI-standardized 'shiny' applications.  
    It brings enhanced features such as 'bootstrap' v4 <https://getbootstrap.com/docs/4.0/getting-started/introduction/>,
    additional and enhanced 'shiny' modules, customizable UI features, as well as an enhanced application file
    organization paradigm. This update allows developers to harness the ability to build powerful applications and 
    enriches the 'shiny' developers' experience when building and  maintaining applications.

A Shiny Web Application to predict and visualize concentrations of pharmaceuticals in the aqueous environment. 
    Jagadeesan K., Barden R. and Kasprzyk-Hordern B. (2022) <https://www.ssrn.com/abstract=4306129>.

Perform Exact or Asymptotic permutation tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].

This version of the permutational algorithm generates a
        dataset in which event and censoring times are conditional on
        an user-specified list of covariates, some or all of which are
        time-dependent.

Calculates multivariate analysis of variance based on permutations and some associated pictorial representations. The pictorial representation is based on the principal coordinates of the group means. There are some original results that will be published soon.

Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.