Title: Access Crime Data from the Open Crime Database
Authors@R: person("Matthew", "Ashby", email = "matthew.ashby@ntu.ac.uk",
Description: Gives convenient access to publicly available police-recorded open
crime data from large cities in the United States that are included in the
Imports: digest, httr, magrittr, purrr, readr, stringr, tibble, dplyr
Packaged: 2018-10-19 15:13:24 UTC; mattashby
Author: Matthew Ashby [aut, cre] (<https://orcid.org/0000-0003-4201-9239>>)

Imports: digest, httr, magrittr, purrr, readr, stringr, tibble, dplyr,
Packaged: 2019-03-21 23:00:43 UTC; mattashby

Authors@R: person("Matthew", "Ashby", email = "matthew.ashby@ucl.ac.uk",
Imports: digest, dplyr, httr, purrr, rlang, sf, stringr
Packaged: 2022-06-18 22:18:45 UTC; mattashby
Author: Matthew Ashby [aut, cre, cph] (<https://orcid.org/0000-0003-4201-9239>)

Title: Statistical Methods for Crime Series Linkage
person("Michael", "Porter", email = "mporter@cba.ua.edu", role = c("aut", "cre")),
person("Brian", "Reich", email = "brian_reich@ncsu.edu", role = "aut"))
Description: Statistical Methods for Crime Series Linkage. This package provides
code for criminal case linkage, crime series identification, crime series

Imports: igraph, geosphere, grDevices, graphics, stats, utils

Title: A Comprehensive Set of Functions to Clean, Analyze, and Present
Description: A collection of functions that make it easier to understand crime (or other)
data, and assist others in understanding it. The package helps you read data
from various sources, clean it, fix column names, and graph the data.
Imports: dplyr, ggplot2, gridExtra, scales, magrittr, gt, grDevices,
tidyr, stats, data.table, lubridate, janitor
Suggests: spelling, testthat (>= 2.1.0), covr

Imports: dplyr, ggplot2, readr, gridExtra, scales, magrittr, gt,

Title: Fits a Model that Partitions the Covariate Space into Blocks in
Description: Implements convex regression with interpretable sharp partitions
(CRISP), which considers the problem of predicting an outcome variable on the basis of two covariates, using an interpretable yet non-additive model. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. More details are provided in Petersen, A., Simon, N., and Witten, D. (2016). Convex Regression with Interpretable Sharp Partitions. Journal of Machine Learning Research, 17(94): 1-31 <http://jmlr.org/papers/volume17/15-344/15-344.pdf>.
Imports: Matrix, MASS, stats, methods, grDevices, graphics
Packaged: 2017-01-04 15:50:10 UTC; ashleypetersen