The King's Health Questionnaire (KHQ) is a disease-specific,
self-administered questionnaire designed specific to assess the impact of
Urinary Incontinence (UI) on Quality of Life. The questionnaire was developed
by Kelleher and collaborators (1997) <doi:10.1111/j.1471-0528.1997.tb11006.x>.
It is a simple, acceptable and reliable measure to use in the clinical setting
and a research tool that is useful in evaluating UI treatment outcomes.
The KHQ five dimensions (KHQ5D) is a condition-specific preference-based
measure developed by Brazier and collaborators (2008) <doi:10.1177/0272989X07301820>.
Although not as popular as the SF6D <doi:10.1016/S0895-4356(98)00103-6> and
EQ-5D <https://euroqol.org/>, the KHQ5D measures health-related quality of
life (HRQoL) specifically for UI, not general conditions like the others
two instruments mentioned. The KHQ5D ca be used in the clinical and economic
evaluation of health care. The subject self-rates their health in terms of
five dimensions: Role Limitation (RL), Physical Limitations (PL), Social
Limitations (SL), Emotions (E), and Sleep (S). Frequently the states on these
five dimensions are converted to a single utility index using country specific
value sets, which can be used in the clinical and economic evaluation of
health care as well as in population health surveys. This package provides
methods to calculate scores for each dimension of the KHQ; converts KHQ item
scores to KHQ5D scores; and also calculates the utility index of the KHQ5D.

person(given = "Zongrui",
family = "Dai",
role = c("aut", "cre"),
email = "dzr17723980497@gmail.com",
comment = c(ORCID = "https://orcid.org/0000-0002-7893-5004"))
Provides the function to calculate the kernel-lasso expansion, Z-score, and max-min-scale standardization.It can increase the dimension of existed dataset and remove abundant features by lasso. Z Dai, L Jiayi, T Gong, C Wang (2021) <doi:10.1088/1742-6596/1955/1/012047>.

Kernel Measure of Multi-Sample Dissimilarity
person("Zhen", "Huang", , "zh2395@columbia.edu", role = c("aut", "cre"))
Implementations of the kernel measure of multi-sample dissimilarity (KMD) between
several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD
measures the dissimilarity between multiple samples, based on the observations from them.
It converges to the population quantity (depending on the kernel) which is between 0 and 1.
A small value indicates the multiple samples are from the same distribution, and a large value
indicates the corresponding distributions are different. The population quantity is 0 if and only
if all distributions are the same, and 1 if and only if all distributions are mutually singular.
The package also implements the tests based on KMD for H0: the M distributions are equal
against H1: not all the distributions are equal. Both permutation test and asymptotic test are
available. These tests are consistent against all alternatives where at least two samples have
different distributions. For more details on KMD and the associated tests, see Huang, Z. and
B. Sen (2022) <arXiv:2210.00634>.