Title: Customizable Higher-Order Recipe Step Functions
Authors@R: person("Lars", "Kjeldgaard", email = "lars_kjeldgaard@hotmail.com", role = c("aut", "cre"))
Description: Customizable higher-order recipe step functions for
the 'recipes' package. These step functions take 'prep' and 'bake' helper
functions as inputs and create specifications of customized recipe steps
Imports: recipes, dplyr, tibble, magrittr, purrr, tidyselect, methods,

Title: CUSUM Charts for Monitoring of Hospital Performance
Description: Provides functions for constructing and evaluating
CUSUM charts and RA-CUSUM charts with focus on false signal probability.
Author: Lena Hubig [aut, cre] (<https://orcid.org/0000-0002-9454-1232>)

Packaged: 2019-03-15 12:40:21 UTC; schneiderheinze

Packaged: 2019-03-19 09:04:40 UTC; schneiderheinze

Imports: stats, graphics, checkmate, data.table, Rcpp
Packaged: 2019-06-17 15:30:53 UTC; schneiderheinze

Title: Cumulative Sum (CUSUM) Charts for Monitoring of Hospital
Suggests: testthat, knitr, dplyr, ggplot2, rmarkdown
Packaged: 2019-08-28 08:47:04 UTC; schneiderheinze

Suggests: testthat, knitr, ggplot2, rmarkdown
Packaged: 2019-10-02 08:07:45 UTC; schneiderheinze

Description: Create CUSUM (cumulative sum) statistics from a vector or dataframe.
Also create single or faceted CUSUM control charts, with or without control limits.
Accepts vector, dataframe, tibble or data.table inputs.
Suggests: covr, dplyr, knitr, rmarkdown, testthat (>= 3.0.0), tibble

Title: Seek the Significant Cutoff Value
Description: Seek the significant cutoff value for a continuous variable, which will
be transformed into a classification, for linear regression,
logistic regression, logrank analysis and cox regression. First of all,
all combinations will be gotten by combn() function. Then n.per argument,
abbreviated of total number percentage, will be used to remove the combination
of smaller data group. In logistic, Cox regression and logrank analysis,
we will also use p.per argument, patient percentage, to filter the lower
proportion of patients in each group. Finally, p value in regression
results will be used to get the significant combinations and output
relevant parameters. In this package, there is no limit to the number of
cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods,
typical Bonferroni and Duglas G (1994) <doi: 10.1093/jnci/86.11.829>, to
adjust the p value, Missing values will be deleted by na.omit() function