Packaged: 2013-02-26 17:51:13 UTC; david_kahle

Packaged: 2013-02-27 16:11:02 UTC; david_kahle

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.
Imports: stringr, rSymPy, rJython, rJava, rjson, partitions, plyr,
Packaged: 2014-02-26 16:18:36 UTC; david_kahle

Packaged: 2014-04-29 13:46:08 UTC; david_kahle

Title: Symbolic Computation and More with Multivariate Polynomials
Authors@R: person("David", "Kahle", email = "david.kahle@gmail.com", role = c("aut", "cre"))
Description: Data structures, operations, and functions for the symbolic
Imports: rSymPy, rJython, rJava, rjson, partitions, plyr, stats
Packaged: 2015-04-30 23:11:12 UTC; david_kahle

Description: Symbolic computing with multivariate polynomials in R.
Imports: rSymPy, rJython, rJava, rjson, partitions, plyr, stats,
Packaged: 2015-12-31 05:09:16 UTC; david_kahle

Packaged: 2016-04-30 18:28:37 UTC; david_kahle

Imports: partitions, plyr, stats, ggplot2, polynom, orthopolynom, tidyr
Packaged: 2016-07-08 15:12:07 UTC; david_kahle

Packaged: 2016-07-13 17:21:35 UTC; david_kahle

Packaged: 2017-05-07 01:16:18 UTC; david_kahle

Packaged: 2017-06-21 15:56:48 UTC; david_kahle

Imports: stringr (>= 1.0.0), partitions, plyr, stats, ggplot2, polynom,
Packaged: 2019-01-31 18:51:19 UTC; david_kahle

Authors@R: person("David", "Kahle", email = "david@kahle.io", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-9999-1558"))
Imports: stringr (>= 1.0.0), stringi, partitions, plyr, stats, ggplot2,
Suggests: testthat (>= 2.1.0), magrittr, dplyr, covr
Packaged: 2020-02-19 19:28:27 UTC; david_kahle
Author: David Kahle [aut, cre] (<https://orcid.org/0000-0002-9999-1558>)

Title: Power Analysis via Monte Carlo Simulation for Correlated Data
Description: 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>.
Imports: abind, boot, dplyr, doSNOW, foreach, ggplot2, MASS, magrittr,
parallel, purrr, snow, sbgcop, rlang, reshape2, tibble, tidyr,
Suggests: BMA, bkmr, bws, infinitefactor, knitr, NHANES, qgcomp,