Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arXiv:2007.08623v1>.

Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.

Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.

A fast and improved implementation of the graphical LASSO.

Tools for creating animated glassmorphism-style tab
    navigation and select filter widgets in 'Shiny' applications.
    Provides a tab navigation component with a sliding glass halo
    animation, a searchable multi-select dropdown, and a single-select
    dropdown - all with multiple colour themes and server-side update
    helpers. Tabs support icons, numeric badges, disable/enable toggling,
    runtime append/remove, reactive rendering via 'renderGlassTabs()', URL
    bookmarking, and compact mode for dashboard card layouts.
    'glassTabCondition()' generates 'conditionalPanel()' condition strings
    without needing to recall the internal input key pattern.
    'glasstabs_news()' displays the release notes from the R console.
    Built-in example apps can be launched with 'runGlassExample()'.  All
    widgets are compatible with standard 'Shiny' layouts and 'bs4Dash'
    dashboards and 'bslib' themed applications.  For full documentation
    and examples see Arthur (2026) <https://prigasg.github.io/glasstabs/>.

Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.