Provides functions for computing moments and coefficients related to the Beta-Wishart and Inverse Beta-Wishart distributions. 
    It includes functions for calculating the expectation of matrix-valued functions of the Beta-Wishart distribution, 
    coefficient matrices C_k and H_k, expectation of matrix-valued functions of the inverse Beta-Wishart distribution, 
    and coefficient matrices \tilde{C}_k and \tilde{H}_k. For more details, refer Hillier and Kan (2024) <https://www-2.rotman.utoronto.ca/~kan/papers/wishmom.pdf>, 
    "On the Expectations of Equivariant Matrix-valued Functions of Wishart and Inverse Wishart Matrices".

Adds ... to a function's argument list so that it can
    tolerate non-matching arguments.

Lets you temporarily execute an expression or a local block with a different here() root in the 'here' package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.

A set of functions to run code 'with' safely and temporarily
    modified global state. Many of these functions were originally a part
    of the 'devtools' package, this provides a simple package with limited
    dependencies to provide access to these functions.

A comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', 'MongoDB', 'Qualtrics') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) <doi:10.20900/jpbs.20210011>.

Provides a minimal R and C++ API for parsing
  well-known binary and well-known text representation of
  geometries to and from R-native formats.
  Well-known binary is compact
  and fast to parse; well-known text is human-readable
  and is useful for writing tests. These formats are
  useful in R only if the information they contain can be
  accessed in R, for which high-performance functions
  are provided here.