Creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.

Introduces the 'symbolicQspray' objects. Such an object
    represents a multivariate polynomial whose coefficients are fractions of
    multivariate polynomials with rational coefficients. The package
    allows arithmetic on such polynomials. It is based on the 'qspray' and 
    'ratioOfQsprays' packages. Some functions for 'qspray' polynomials have
    their counterpart for 'symbolicQspray' polynomials. A 'symbolicQspray' 
    polynomial should not be seen as a polynomial on the field of fractions 
    of rational polynomials, but should rather be seen as a polynomial with 
    rational coefficients depending on some parameters, symbolically 
    represented, with a dependence given by fractions of rational polynomials.

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.