Johan Zetterqvist <drjohanzetterqvist at gmail.com> , Arvid Sjölander <arvid.sjolander at ki.se> with contributions from Alexander Ploner.
Fit restricted mean models for the conditional association
between an exposure and an outcome, given covariates. Three methods
are implemented: O-estimation, where a nuisance model for the
association between the covariates and the outcome is used;
E-estimation where a nuisance model for the association
between the covariates and the exposure is used, and doubly robust (DR)
estimation where both nuisance models are used. In DR-estimation,
the estimates will be consistent when at least one of the nuisance
models is correctly specified, not necessarily both. For more information, see Zetterqvist and Sjölander (2015) <doi:10.1515/em-2014-0021>.

stats, nleqslv, survival, R (>= 3.0.0), Rcpp, data.table

c(person("Yue", "Hu", email = "yuehu@tsinghua.edu.cn", role = c("aut","cre")), person("Yufei", "Sun", email = "sunyf20@mails.tsinghua.edu.cn", role = c("ctb")))
A fast, interactive tool built upon the 'learnr' function which will open a shiny app for learners to interact with the instructions and tasks. The best way to learn these skills together with the “Learning R with Dr. Hu” online/offline workshops.
gapminder, scales, gt, modelsummary, dotwhisker, interplot,
likert, ggthemes, ggeffects, summarytools, knitr, tidyverse,
car, arm, nnet, ordinal, moments, tibble, performance,
kableExtra

c(
person("Yue", "Hu", role = c("aut", "cre"), email = "yuehu@tsinghua.edu.cn"),
person("Yufei", "Sun", role = c("aut"), email = "sunyf20@mails.tsinghua.edu.cn")
)
Tutorials of R learning easily and happily.
rmarkdown, scales, modelsummary, likert, knitr, tidyverse,
kableExtra

c(
person("Andrew", "Shaughnessy", , "andrew.shaughnessy@slu.edu", c("aut", "cre")),
person("Christopher", "Prener", , "chris.prener@slu.edu", c("aut")),
person("Elizabeth", "Hasenmueller", , "elizabeth.hasenmueller@slu.edu", c("aut"))
)
A tidy implementation of equations that correct for instrumental drift in
continuous water quality monitoring data.  There are many sources of water quality data
including private (ex: YSI instruments) and open source (ex: USGS and NDBC), each of
which are susceptible to errors/inaccuracies due to drift. This package allows the
user to correct their data using one or two standard reference values in a uniform,
reproducible way. The equations implemented are from Hasenmueller (2011)
<doi:10.7936/K7N014KS>.
dplyr, ggplot2, readr, stringr, rlang, tibble, magrittr, glue
Andrew Shaughnessy [aut, cre],
Christopher Prener [aut],
Elizabeth Hasenmueller [aut]