Suite of Functions to Help Get Plot Elements Exactly Where You
Want Them
Provides a selection of tools that make it easier to place elements onto a (base R) plot exactly where you want them. It allows users to identify points and distances on a plot in terms of inches, pixels, margin lines, data units, and proportions of the plotting space, all in a manner more simple than manipulating par().

Estimate Sample Size Based on Precision Rather than Power
person("Malcolm", "Barrett",
email = "malcolmbarrett@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-0299-5825"))
Estimate sample size based on precision rather than
power. 'precisely' is a study planning tool to calculate sample size
based on precision. Power calculations are focused
on whether or not an estimate will be statistically significant;
calculations of precision are based on the same principles as power
calculation but turn the focus to the width of the confidence
interval. 'precisely' is based on the work of Rothman and Greenland
(2018) <doi: 10.1097/EDE.0000000000000876>.
dplyr, ggplot2, magrittr, purrr, rlang, shiny,
shinycssloaders, shinythemes, tidyr
ggrepel, knitr, rmarkdown, spelling, testthat, vdiffr, covr

Estimate sample size based on precision rather than power.
'precisely' is a study planning tool to calculate sample size based on
precision. Power calculations are focused on whether or not an
estimate will be statistically significant; calculations of precision
are based on the same principles as power calculation but turn the
focus to the width of the confidence interval. 'precisely' is based on
the work of 'Rothman and Greenland' (2018).
covr, ggrepel, knitr, rmarkdown, spelling, testthat, vdiffr

c(person(given = "Lu",
family = "Tian",
email = "lutian@stanford.edu",
role = "aut",
comment="https://orcid.org/0000-0002-5893-0169"),
person(given = "Xiaotong",
family = "Jiang",
email = "xiaotong.phoebe.jiang@gmail.com",
role = "aut",
comment="https://orcid.org/0000-0003-3698-4526"),
person(given = "Gabrielle",
family = "Simoneau",
email = "gabrielle.simoneau@mail.mcgill.ca",
role = "aut",
comment="https://orcid.org/0000-0001-9310-6274"),
person("Biogen  MA  Inc.", role="cph"),
person(given = "Thomas",
family = "Debray",
role = c("ctb", "cre"),
email = "tdebray@fromdatatowisdom.com",
comment="https://orcid.org/0000-0002-1790-2719"),
person(given = "Stan",
family = "Wijn",
role = "ctb"),
person(given = "Joana",
family = "Caldas",
role = "ctb"))
A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.
dplyr, gbm, gam, ggplot2, glmnet, graphics, MASS, MESS, mgcv,
rlang, stringr, tidyr, survival, randomForestSRC
Lu Tian [aut] (<https://orcid.org/0000-0002-5893-0169>),
Xiaotong Jiang [aut] (<https://orcid.org/0000-0003-3698-4526>),
Gabrielle Simoneau [aut] (<https://orcid.org/0000-0001-9310-6274>),
Biogen MA Inc. [cph],
Thomas Debray [ctb, cre] (<https://orcid.org/0000-0002-1790-2719>),
Stan Wijn [ctb],
Joana Caldas [ctb]