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.

Tests for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate. Details are described in Parast et al (2022) <arXiv:2209.08315>.

Computes diagnostics for linear regression when treatment effects are heterogeneous.
    The output of 'hettreatreg' represents ordinary least squares (OLS) 
    estimates of the effect of a binary treatment as a weighted average of the average treatment effect 
    on the treated (ATT) and the average treatment effect on the untreated (ATU). 
    The program estimates the OLS weights on these parameters, computes the associated model diagnostics, 
    and reports the implicit OLS estimate of the average treatment effect (ATE). 
    See Sloczynski (2019), <http://people.brandeis.edu/~tslocz/Sloczynski_paper_regression.pdf>.