An implementation of White's Test for Heteroskedasticity
as outlined in Doornik (1996).

Heteroskedastic Gaussian Process Modeling and Design under
Replication
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
Rcpp (>= 0.12.3), MASS, methods, DiceDesign

Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

Assess the presence of statistical heterogeneity and quantify its impact in the context of meta-analysis. It includes test for heterogeneity as well as other statistical measures (R_b, I^2, R_I).

Assessing Heterogeneity in the Utility of a Surrogate Marker
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.

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

Testing for a Treatment Effect Using a Heterogeneous Surrogate
Marker
c(person("Layla", "Parast", role = c("aut", "cre"), email = "parast@austin.utexas.edu"))
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>.