Provides R6 classes, methods and utilities to construct,
analyze, summarize, and visualize regression models.
broom.helpers, data.table, dplyr, forestploter, glue,
parameters, R6, rlang (>= 0.4.11), stats, survival

parallel, roxygen2, see, testthat (>= 3.0.0)

The package provides tools for
(1) nonparametric regression (kernel, local linear),
(2) semiparametric regression (single index, additive models), and
(3) quantile regression (linear, kernel)

Tools are provided for
(1) nonparametric regression (kernel, local linear),
(2) semiparametric regression (single index, additive models), and
(3) quantile regression (linear, kernel).

Easy Linear, Quadratic and Cubic Regression Models
Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.

c(
person("Jiangtao", "Gou", email = "gouRpackage@gmail.com", role = c("aut", "cre")),
person("Fengqing", "Zhang", role = "aut"))
The graphical approach is proposed as a general framework for clinical trial designs involving multiple hypotheses, where decisions are made only based on the observed marginal p-values. A reverse graphical approach starts from a set of singleton graphs, and gradually add vertices into graphs until rejection of a set of hypotheses is made. See Gou, J. (2020). Reverse graphical approaches for multiple test procedures. Technical Report.
Jiangtao Gou [aut, cre],
Fengqing Zhang [aut]

Fitting Gaussian Linear Models where the Covariance Structure is a Linear Combination of Known Matrices by Maximising the Residual Log Likelihood
David Clifford <clifford@galton.uchicago.edu>, Peter McCullagh <pmcc@galton.uchicago.edu>
Functions to fit Gaussian linear model by maximising the residual log likelihood.  The covariance structure can be written as a linear combination of known matrices.  Can be used for multivariate models and random effects models.  Easy straight forward manner to specify random effects models, including random interactions.