R (>= 2.7.0), randtoolbox, orthopolynom, ks, lars
A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.

Gaussian Processes for Estimating Causal Exposure Response
Curves
c(
person("Naeem", "Khoshnevis", email = "nkhoshnevis@g.harvard.edu",
role=c("aut","cre"),
comment = c(ORCID = "0000-0003-4315-1426", AFFILIATION="FASRC")),
person("Boyu", "Ren", email = "bren@partners.org",
role= c("aut"),
comment = c(ORCID = "0000-0002-5300-1184", AFFILIATION="McLean Hospital")),
person("Tanujt", "Dey", email = "tdey@bwh.harvard.edu",
role= c("ctb"),
comment = c(ORCID = "0000-0001-5559-211X", AFFILIATION="HMS")),
person("Danielle","Braun", email = "dbraun@hsph.harvard.edu",
role=c("aut"),
comment = c(ORCID = "0000-0002-5177-8598", AFFILIATION="HSPH")))
Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <arXiv:2105.03454>.
parallel, data.table, xgboost, stats, MASS, spatstat.geom,
logger, Rcpp, ggplot2, rlang, Matrix
Naeem Khoshnevis [aut, cre] (<https://orcid.org/0000-0003-4315-1426>,
FASRC),
Boyu Ren [aut] (<https://orcid.org/0000-0002-5300-1184>, McLean
Hospital),
Tanujt Dey [ctb] (<https://orcid.org/0000-0001-5559-211X>, HMS),
Danielle Braun [aut] (<https://orcid.org/0000-0002-5177-8598>, HSPH)

Differential Item Functioning in Generalized Partial Credit
Models
Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
Rcpp (>= 0.12.4), wordcloud, TeachingDemos, cubature, car,
caret, statmod, mvtnorm, mirt, methods