CUSUM Charts for Monitoring of Hospital Performance
c(
person("Lena", "Hubig",
email = "cs@lenahubig.de",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9454-1232")))
Provides functions for constructing and evaluating
CUSUM charts and RA-CUSUM charts with focus on false signal probability.

stats, graphics, checkmate, data.table, Rcpp

Cumulative Sum (CUSUM) Charts for Monitoring of Hospital
Performance
testthat, knitr, dplyr, ggplot2, rmarkdown

person(given = "John",
family = "MacKintosh",
role = c("aut", "cre"),
email = "johnmackintosh.jm@gmail.com")
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.
covr, dplyr, knitr, rmarkdown, testthat (>= 3.0.0), tibble

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
before analysis.

CUTOFF: A Spatio-temporal Imputation Method
Lingbing Feng, Gen Nowak, Alan. H. Welsh, Terry. J. O'Neill
This package provides a set of tools for spatio-temporal imputation
in R. It includes the implementation for then CUTOFF imputation method,
a useful cross-validation function that can be used not only by the
CUOTFF method but also by some other imputation  functions to help
choosing an optimal value for relevant parameters, such as the number
of k-nearest neighbors for the KNN imputation method, or the number of
components for the SVD imputation method. It also contains tools for
simulating data with missing values with respect to some specific
missing pattern, for example, block missing. Some useful visualisation
functions for imputation purposes are also provided in the package.