'as.function.mpoly.R' 'as.function.mpolyList.R'
'deriv.mpoly.R' 'grobner.R' 'is.mpoly.R' 'is.mpolyList.R'
'is.wholenumber.R' 'LCM.R' 'mp.R' 'mpoly.R' 'mpolyAdd.R'
'mpolyEqual.R' 'mpolyExp.R' 'mpolyList.R' 'mpolyListAdd.R'
'mpolyListProd.R' 'mpolyProd.R' 'print.mpoly.R'
'print.mpolyList.R' 'reorder.mpoly.R' 'terms.mpoly.R' 'vars.R'
'help.R' 'permutations.R' 'tuples.R' 'partitions.R' 'burst.R'
'as.mpoly.R'

mpoly allows for the symbolic manipulation of multivariate
polynomials in a general and consistent way designed especially for
sparse and super sparse polynomials.  It also allows for basic
differential calculus with multivariate polynomials and Grobner basis
calculations from computational algebraic geometry.
stringr, rSymPy, rJython, rJava, rjson, partitions, plyr,
stats

Symbolic Computation and More with Multivariate Polynomials
person("David", "Kahle", email = "david.kahle@gmail.com", role = c("aut", "cre"))
Data structures, operations, and functions for the symbolic
manipulation of multivariate polynomials.
rSymPy, rJython, rJava, rjson, partitions, plyr, stats

Symbolic computing with multivariate polynomials in R.
rSymPy, rJython, rJava, rjson, partitions, plyr, stats,
ggplot2, polynom, orthopolynom, reshape2

rSymPy, rJython, rJava, rjson, partitions, plyr, stats,
ggplot2, polynom, orthopolynom, tidyr

partitions, plyr, stats, ggplot2, polynom, orthopolynom, tidyr

stringr (>= 1.0.0), partitions, plyr, stats, ggplot2, polynom,
orthopolynom, tidyr

person("David", "Kahle", email = "david@kahle.io", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9999-1558"))
stringr (>= 1.0.0), stringi, partitions, plyr, stats, ggplot2,
polynom, orthopolynom, tidyr
testthat (>= 2.1.0), magrittr, dplyr, covr

Power Analysis via Monte Carlo Simulation for Correlated Data
A flexible framework for power analysis using Monte
Carlo simulation for settings in which considerations of the correlations
between predictors are important. Users can set up a data generative model
that preserves dependence structures among predictors given existing data
(continuous, binary, or ordinal). Users can also generate power curves to
assess the trade-offs between sample size, effect size, and power of a design.
This package includes several statistical models common in environmental
mixtures studies. For more details and tutorials, see Nguyen et al. (2022) <arXiv:2209.08036>.
abind, boot, dplyr, doSNOW, foreach, ggplot2, MASS, magrittr,
parallel, purrr, snow, sbgcop, rlang, reshape2, tibble, tidyr,
tidyselect
BMA, bkmr, bws, infinitefactor, knitr, NHANES, qgcomp,
rmarkdown, rstan, testthat, openxlsx