Scales Score Calculation from Quality of Life Data
There are three functions: qol, miss_qol and miss_patient takes input of the data set containing the answers of QOL questionnaire. It will compute the three types of domain based scale scores: Global, Functional, and Symptoms. In case of missing data, the miss_qol and miss_patient functions will make the required changes and then calculate the domain-wise scale scores. Finally, provide an output replacing the question columns with the domain-based scale scores in the original data set.
c(person(("Atanu"), "Bhattacharjee",
email="atanustat@gmail.com",
role=c("aut", "cre","ctb")),
person(("Ankita"), "Pal", role=c("aut","ctb")))
Atanu Bhattacharjee [aut, cre, ctb],
Ankita Pal [aut, ctb]

Analysis of Health-Related Quality of Life in Oncology
To generate the scores of the EORTC QLQ-C30 questionnaire and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.

To generate the scores of the EORTC QLQ-C30 questionnaire <http://www.eortc.be/qol/files/SCManualQLQ-C30.pdf> and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.

Analysis of Health-Related Quality of Life in oncology
To generate the scores of the EORTC QLQ-C30 questionnaire and supplementary modules and to determine the time to quality of life score deterioration in longitudinal analysis.

Block Bootstrap Methods for Quantile Regression in Time Series
Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

utils, grDevices, graphics, stats, MASS, quantreg, mice, boot
This is a collection of functions for unconditional and conditional quantiles.