Package: aoristic
Type: Package
Title: Generates Aoristic Probability Distributions
Version: 1.1.1
Author: Jerry Ratcliffe
Maintainer: Jerry Ratcliffe <jhr@temple.edu>
Description: It can sometimes be difficult to ascertain when some events (such as property crime)
occur because the victim is not present when the crime happens. As a result, police databases often
record a 'start' (or 'from') date and time, and an 'end' (or 'to') date and time. The time span between
these date/times can be minutes, hours, or sometimes days, hence the term 'Aoristic'.
Aoristic is one of the past tenses in Greek and represents an uncertain occurrence in time.
For events with a location describes with either a latitude/longitude, or X,Y coordinate pair,
and a start and end date/time, this package generates an aoristic data frame with aoristic weighted
probability values for each hour of the week, for each observation. The coordinates are not
necessary for the program to calculate aoristic weights; however, they are part of this package
because a spatial component has been integral to aoristic analysis from the start. Dummy
coordinates can be introduced if the user only has temporal data. Outputs include an aoristic
data frame, as well as summary graphs and displays.
For more information see:
Ratcliffe, JH (2002) Aoristic signatures and the temporal analysis of high volume crime patterns,
Journal of Quantitative Criminology. 18 (1): 23-43.
Note: This package replaces an original 'aoristic' package (version 0.6) by George Kikuchi that
has been discontinued with his permission.
License: GPL-3
Encoding: UTF-8
Imports: openxlsx, ggplot2, grid, lubridate, plyr, scales, tidyr,
dplyr, stats
LazyData: true
RoxygenNote: 7.2.1
Depends: R (>= 2.10)
NeedsCompilation: no
Packaged: 2022-10-20 19:47:05 UTC; jhr
Repository: CRAN
Date/Publication: 2022-10-20 20:15:06 UTC

Title: Accelerated Oblique Random Survival Forests
Description: Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2022) <arXiv:2208.01129>.
Suggests: survival, survivalROC, ggplot2, testthat (>= 3.0.0), knitr,
Author: Byron Jaeger [aut, cre] (<https://orcid.org/0000-0001-7399-2299>),