Provides functions to run statistical analyses on
    surface-based neuroimaging data, computing measures including cortical
    thickness and surface area of the whole-brain and of the hippocampi.
    It can make use of 'FreeSurfer', 'fMRIprep', 'XCP-D', 'HCP' and 'CAT12' preprocessed
    datasets and 'HippUnfold' hippocampal segmentation outputs for a given
    sample by restructuring the data values into a single file. The single
    file can then be used by the package for analyses independently from
    its base dataset and without need for its access.

Multi-precision library that allows to store and operate with arbitrarily big integers without
    loss of precision. It includes a large list of tools to work with them, like:
      - Arithmetic and logic operators
      - Modular-arithmetic operators
      - Computer Number Theory utilities
      - Probabilistic primality tests
      - Factorization algorithms
      - Random generators of diferent types of integers.

The goal of 'vetiver' is to provide fluent tooling to
    version, share, deploy, and monitor a trained model. Functions handle
    both recording and checking the model's input data prototype, and
    predicting from a remote API endpoint. The 'vetiver' package is
    extensible, with generics that can support many kinds of models.

Declarative template-based framework for verifying that objects
  meet structural requirements, and auto-composing error messages when they do
  not.

R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

Implements methods for inference on potential waning of vaccine
    efficacy and for estimation of vaccine efficacy at a user-specified time
    after vaccination based on data from a randomized, double-blind,
    placebo-controlled vaccine trial in which participants may be unblinded
    and placebo subjects may be crossed over to the study vaccine.  The
    methods also allow adjustment for possible confounding via inverse
    probability weighting through specification of models for the trial
    entry process, unblinding mechanisms, and the probability an unblinded
    placebo participant accepts study vaccine:  Tsiatis, A. A. and Davidian,
    M. (2022) <doi:10.1111/biom.13509>.