A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.

Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.

Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.

Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.

Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.

Easily analyze and visualize the performance of symptom
    checkers. This package can be used to gain comprehensive insights into
    the performance of single symptom checkers or the performance of
    multiple symptom checkers. It can be used to easily compare these
    symptom checkers across several metrics to gain an understanding of
    their strengths and weaknesses. The metrics are developed in 
    Kopka et al. (2023) <doi:10.1177/20552076231194929>.

A collection of functions for symbolic computation using the
			 'caracas' package for structural equation models and other
			 statistical analyses. Among its features is the ability
			 to calculate the model-implied covariance (and correlation)
			 matrix and the sampling covariance matrix of variable functions
			 using the delta method.