Find non-linear formulas that fits your input data. You can systematically explore and memorize the possible formulas and it's cross-validation performance, in an incremental fashion. Three main interoperable search functions are available: 1) random.search() performs a random exploration, 2) genetic.search() employs a genetic optimization algorithm, 3) comb.search() combines best results of the first two. For more details see Tomasoni et al. (2026) <doi:10.1208/s12248-026-01232-z>.

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.

Provides implementations of origin-based and symmetrized minimum covariance determinant (MCD) estimators, together with supporting utility functions.

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.

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>.