Homomorphic encryption (Brakerski and Vaikuntanathan (2014) <doi:10.1137/120868669>) using Ring Learning with Errors (Lyubashevsky et al. (2012) <https://eprint.iacr.org/2012/230>) is a form of Learning with Errors (Regev (2005) <doi:10.1145/1060590.1060603>) using polynomial rings over finite fields. Functions to generate the required polynomials (using 'polynom'), with various distributions of coefficients are provided. Additionally, functions to generate and take coefficient modulo are provided.

Cellular responses to perturbations are highly heterogeneous
    and depend largely on the initial state of cells. Connecting
    post-perturbation cells via cellular trajectories to untreated cells
    (e.g. by leveraging metabolic labeling information) enables
    exploitation of intercellular heterogeneity as a combined knock-down
    and overexpression screen to identify pathway modulators, termed
    Heterogeneity-seq (see 'Berg et al' <doi:10.1101/2024.10.28.620481>).
    This package contains functions to generate cellular trajectories based
    on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for
    metabolic labelling sequencing) time courses, functions to identify
    pathway modulators and to visualize the results.

Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.

Provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points.  More details are available in Parast et al (2024) <doi:10.1002/sim.10122>.

Functions for the fitting and summarizing of heteroscedastic t-regression.