Description: A collection of functions that perform operations on time-series accelerometer data, such as identify non-wear time, flag minutes that are part of an activity bout, and find the maximum 10-minute average count value. The functions are generally very flexible, allowing for a variety of algorithms to be implemented. Most of the functions are written in C++ for efficiency.

Title: Functions for Processing Accelerometer Data

Title: Missing Value Imputation for Accelerometer Data
Author: Jung Ae Lee <jungaeleeb@gmail.com>
Maintainer: Jung Ae Lee <jungaeleeb@gmail.com>
Description: Imputation for the missing count values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. This package also provides multiple functions to pre-process the accelerometer data previous to the missing data imputation. These includes detecting wearing and non-wearing time, selecting valid days and subjects, and creating plots.

Title: The Acute COPD Exacerbation Prediction Tool (ACCEPT)
person("Amin", "Adibi", email = "adibi@alumni.ubc.ca", role = c("aut", "cre")),
person("Mohsen", "Sadatsafavi", email = "mohsen.sadatsafavi@ubc.ca", role = c("aut", "cph")),
person("Ainsleigh", "Hill", email = "ainsleigh.hill@alumni.ubc.ca", role = c("aut")))
Description: Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction model published in Adibi et al. (2019) <doi:10.1101/651901>.
Imports: stats, MASS, dplyr, stringr, extrafont, plotly, viridis

Description: Allows clinicians to predict the rate and severity of future acute exacerbation in Chronic Obstructive Pulmonary Disease (COPD) patients, based on the clinical prediction model published in Adibi et al. (2020) <doi:10.1016/S2213-2600(19)30397-2>.

person("Abdollah", "Safari", email = "asafari@mail.ubc.ca", role = c("aut")),

person(given = "Ipea - Institute for Applied Economic Research",
Description: A set of fast and convenient functions to calculate multiple
transport accessibility measures. Given a pre-computed travel cost
matrix in long format combined with land-use  data (e.g. location
of  jobs, healthcare, population), the package  allows one to
calculate active and passive accessibility levels using multiple
accessibility metrics such as: cumulative opportunity measure
(using either travel time cutoff or interval), minimum travel cost
to closest N number of activities, gravitational measures and
different floating catchment area methods.
Suggests: covr, ggplot2, knitr, r5r, rmarkdown, sf, stats, testthat
Ipea - Institute for Applied Economic Research [cph, fnd]
Maintainer: Rafael H. M. Pereira <rafa.pereira.br@gmail.com>