When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/regression/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.

Streamlines 'Quarto' workflows by providing tools for consistent project setup and documentation. Enables portability through reusable metadata, automated project structure creation, and standardized templates. Features include enhanced project initialization, pre-formatted 'Quarto' documents, inclusion of 'Quarto' brand functionality, comprehensive data protection settings, custom styling, and structured documentation generation. Designed to improve efficiency and collaboration in R data science projects by reducing repetitive setup tasks while maintaining consistent formatting across multiple documents.

Provides a set of helper functions for constructing file paths 
    relative to the root of various types of projects, such as R packages, Git
    repositories, and more. File paths are specified with function arguments,
    or `$` to navigate into folders to specific files supported by 
    auto-completion.

Fast, numerically robust computation of weighted moments via 'Rcpp'. 
   Supports computation on vectors and matrices, and Monoidal append of moments. 
   Moments and cumulants over running fixed length windows can be computed, 
   as well as over time-based windows.
   Moment computations are via a generalization of Welford's method, as described
   by Bennett et. (2009) <doi:10.1109/CLUSTR.2009.5289161>.

Maximum Likelihood Estimation of
   Stochastic Frontier Production and Cost Functions.
   Two specifications are available:
   the error components specification with time-varying efficiencies
   (Battese and Coelli, 1992, <doi:10.1007/BF00158774>)
   and a model specification in which the firm effects are directly 
   influenced by a number of variables (Battese and Coelli, 1995,
   <doi:10.1007/BF01205442>).