HOT: The Health-Oriented Transportation (HOT) Model

#' Health-Oriented Transportation (HOT) Model
#'
#'

#' HOT is a statistical model that makes projections about
#' attributable health risks from (in)active travel data. Based on
#' population and travel scenarios, the model has been used to
#' calculate the health impacts of walking and bicycling short
#' distances usually traveled by car or driving low-emission
#' automobiles.  Please Cite: Younkin, S. (2019).
#'
#' The model uses comparative risk assessment through which it
#' formulates a change in the disease burden, resulting from the shift
#' in the exposure distribution from a baseline scenario to an
#' alternative scenario.
#'
#' HOT characterizes exposure distributions in several ways:
#'
#' -- Physical Activity --
#' Described as quintiles of a log-normal distribution on the basis of
#' the mean weekly active transport time per person, its standard
#' deviation and coefficient of variation (the standard deviation
#' divided by the mean), mean weekly non-transport physical activity,
#' and the ratio between bicycling and walking times. The activity
#' times were multiplied by weights to give metabolic-equivalent task
#' hours (METS), which reflect energy expenditures for walking and
#' cycling at average speeds and for performing occupational tasks.
#'
#' Descriptive statistics were obtained from published research on
#' walking and bicycling speeds and analysis of travel and health
#' surveys with large probability samples for the Bay Area.

#' HOT characterizes exposure distributions in several ways:

#' -- Physical Activity -- Described as quintiles of a log-normal
#' distribution on the basis of the mean weekly active transport time
#' per person, its standard deviation and coefficient of variation
#' (the standard deviation divided by the mean), mean weekly
#' non-transport physical activity, and the ratio between bicycling
#' and walking times. The activity times were multiplied by weights to
#' give metabolic-equivalent task hours (METS), which reflect energy
#' expenditures for walking and cycling at average speeds and for
#' performing occupational tasks.  Time spent in either mode (walking
#' or bicycling) was taken from London travel survey data.
#'

#'
#' @name HOT-package
#' @aliases HOT
#' @docType package
#' @author Samuel G. Younkin \email{syounkin@@wisc.edu}
#' @references \url{https://ghi.wisc.edu/health-climate-cities/health-oriented-transportation/}
#' @seealso \code{\link{CRA}}, \code{\link{getTravelActivity}}, \code{\link{getMeans}}
#' @examples
#' # Parametric
#' HOT:::CRA.function(meanlog.baseline = log(10), meanlog.scenario = log(11), type = "parametric")
#' HOT:::CRA.function(participation.baseline = 0.75, participation.scenario = 0.74, type = "parametric")
#'
#' # Non-parametric
#' n <- 1e4
#' P <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(10))
#' Q <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(11))
#' HOT:::CRA.function(P = P, Q = Q, type = "non-parametric")
#' HOT:::CRA.function(meanlog.baseline = log(10), meanlog.scenario = log(11), participation.baseline = 0, participation.scenario = 0, meanlog.Tc = 1e-6, n = n)
#'
#' @import tidyverse utils methods stats reshape2 rgdal rgeos maptools
#' @importFrom plotly ggplotly config
#'
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