Enable the use of 'Shepherd.js' to create tours in 'Shiny' 
    applications.

Exploring fitted models by interactively taking 2-D and 3-D
  sections in data space.

Constructs a shiny app function with interactive displays for conditional visualization of models, 
     data and density functions. An extended version of package 'condvis'. 
    Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.

Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems. See Mary C. Meyer (2013)<doi:10.1080/03610918.2012.659820> for more details.

This small library contains a series of simple tools for
        constructing and manipulating confounded and fractional
        factorial designs.

Calculate confidence and consistency that measure the
    goodness-of-fit and transferability of predictive/potential distribution
    models (including species distribution models) as described by Somodi &
    Bede-Fazekas et al. (2024) <doi:10.1016/j.ecolmodel.2024.110667>.

Functions for estimating and reporting multi-year averages and
    corresponding confidence intervals and distributions. A potential use case
    is reporting the chemical and ecological status of surface waters according
    to the European Water Framework Directive.

Frequentist confidence analysis answers the question: How
    confident are we in a particular treatment effect?  This package calculates
    the frequentist confidence in a treatment effect of interest given observed data,
    and returns the family of
    confidence curves associated with that data.

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.