Estimation of Mean Costs in Censored Data
c(
person("Lars", "Boerty", email = "lars.borty@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3715-8528")),
person("Rasmus", "Broendum", role = "aut", comment = c(ORCID = "0000-0001-5537-6767")),
person("Martin", "Boegsted", role = "aut", comment = c(ORCID = "0000-0001-9192-1814")),
person("Haematology Research Unit - Aalborg", role = "cph"))
Implementation of estimators for inferring
the mean of censored cost data. Including the estimators
BT from Bang and Tsiatis (2000) <doi:10.1093/biomet/87.2.329> and
ZT from Zhao and Tian (2001) <doi:10.1111/j.0006-341X.2001.01002.x>.
rmarkdown, parallel, testthat (>= 2.1.0)
ggplot2, dplyr, tibble, knitr, msm, forcats, rlang,
data.table, survival, Rdpack
Lars Boerty [aut, cre] (<https://orcid.org/0000-0002-3715-8528>),
Rasmus Broendum [aut] (<https://orcid.org/0000-0001-5537-6767>),
Martin Boegsted [aut] (<https://orcid.org/0000-0001-9192-1814>),
Haematology Research Unit - Aalborg [cph]

person(given = "Johannes",
family = "Karl",
role = c("aut", "cre"),
email = "johannes.a.karl@gmail.com")
With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.
magrittr, dplyr, lavaan, readr, MCMCpack, psych, ufs, xlsx,
tibble, rlang, RcppAlgos, tidyr

Correct and Cluster Response Style Biased Data
c(person(given = "Mariko", family = "Takagishi", role = c("aut",
"cre"), email = "m.takagishi0728@gmail.com"))
Functions for performing Correcting and Clustering response-style-biased preference data (CCRS). The main functions are correct.RS() for correcting for response styles, and ccrs() for simultaneously correcting and content-based clustering. The procedure begin with making rank-ordered boundary data from the given preference matrix using a function called create.ccrsdata(). Then in correct.RS(), the response style is corrected as follows: the rank-ordered boundary data are smoothed by I-spline functions, the given preference data are transformed by the smoothed functions. The resulting data matrix, which is considered as bias-corrected data, can be used for any data analysis methods. If one wants to cluster respondents based on their indicated preferences (content-based clustering), ccrs() can be applied to the given (response-style-biased) preference data, which simultaneously corrects for response styles and clusters respondents based on the contents. Also, the correction result can be checked by plot.crs() function.
cds, colorspace, dplyr, graphics, limSolve, lsbclust, methods,
msm, parallel, stats, utils