Simultaneous Inference for Linear Models
Xianyang Zhang, Guang Cheng, Jincheng Bai
Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.
scalreg does not correctly import lars etc, so we need to
depend on it
glmnet, hdi, SIS, stats, parallel, utils

Boosting Method for High-Dimensional Error-Prone Data
Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for (ultra)high-dimensional data subject to measurement error in predictors. Some relevant references include Chen, L.-P. and Yi, G. Y. (2021) <doi:10.1111/biom.13331>, Hastie, T., Tibshirani, R. and Friedman, J. (2008, ISBN:978-0387848570), and Yi, G. Y. (2017, ISBN:978-1493966387).

Set-Based Inference for Multiple Interval-Censored Outcomes
c(
person("Jaihee", "Choi", email = "jaiheechoi01@gmail.com", role = c("aut", "cre")),
person("Ryan", "Sun", role="aut"))
Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.
bindata, ICSKAT, fastGHQuad, CompQuadForm, stats

bindata, fastGHQuad, CompQuadForm, stats, ICSKAT

Subnetwork Integration for Multi-Modal Signatures
Syed Haider, Michal Grzadkowski, Paul C. Boutros
Algorithms to create prognostic biomarkers using
biological networks

Algorithms to create prognostic biomarkers using biological networks.

Syed Haider [aut, cre], Paul C. Boutros [aut], Michal Grzadkowski [ctb]

c(
person("Syed", "Haider", role = c("aut", "cre"), email = "Syed.Haider@icr.ac.uk"),
person("Paul", "C. Boutros", role = "aut"),
person("Michal", "Grzadkowski", role = "ctb")
)
Syed Haider [aut, cre],
Paul C. Boutros [aut],
Michal Grzadkowski [ctb]
Algorithms to create prognostic biomarkers using biological genesets or networks.

Multiple Regression and Moderated Regression Made Simple
Provides SPSS- and SAS-like output for least squares multiple
regression and moderated regression, as well as interaction plots and
Johnson-Neyman regions of significance for interactions. The output
includes standardized coefficients, partial and semi-partial correlations,
collinearity diagnostics, plots of residuals, and detailed information
about simple slopes for interactions. There are numerous options for
designing interaction plots, including plots of interactions for both lm
and lme models.