Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014>.
Rcpp, nloptr, stats, graphics, grDevices, cubature, sn,
mnormt, methods

Rcpp, nloptr, stats, graphics, grDevices, cubature, sn,
mnormt, methods, mvQuad

Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Optimal Designs for Linear and Nonlinear Models
c(person("Ehsan", "Masoudi", role = c("aut", "cre"),  email = "esn_mud@yahoo.com"),  person("Heinz", "Holling", role = "aut"),  person("Weng Kee", "Wong", role = "aut"), person("Seongho", "Kim", role = c("ctb")))
Rcpp, nloptr, stats, utils, graphics, grDevices, cubature, sn,
mnormt, methods, mvQuad
Ehsan Masoudi [aut, cre],
Heinz Holling [aut],
Weng Kee Wong [aut],
Seongho Kim [ctb]

Optimal Designs for Nonlinear Statistical Models by Imperialist
Competitive Algorithm (ICA)
Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Bayesian Semiparametric Models for Interval-Censored Data
Chun Pan, Bo Cai, Lianming Wang, and Xiaoyan Lin
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.

Data-Driven Design of Targeted Gene Panels for Estimating
Immunotherapy Biomarkers
c(person(given = "Jacob R.",
family = "Bradley",
role = c("aut", "cre"),
email = "cobrbradley@gmail.com",
comment = c(ORCID = "0000-0003-1616-4969")),
person(given = "Timothy I.",
family = "Cannings",
role = c("aut"),
email = "Timothy.cannings@sms.ed.ac.uk",
comment = c(ORCID = "0000-0002-2111-4168")))
Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.
stats, utils, glmnet, Matrix, dplyr, purrr, latex2exp,
matrixStats, ggplot2, gglasso, PRROC
Jacob R. Bradley [aut, cre] (<https://orcid.org/0000-0003-1616-4969>),
Timothy I. Cannings [aut] (<https://orcid.org/0000-0002-2111-4168>)