Data on international and other major cricket matches from
  ESPNCricinfo <https://www.espncricinfo.com> and Cricsheet <https://cricsheet.org>.
  This package provides some functions to download the data into tibbles ready
  for analysis.

Tools for analyzing performances of cricketers based on stats in
    ESPN Cricinfo Statsguru. The toolset can  be used for analysis of Tests,ODIs 
    and Twenty20 matches of both batsmen and bowlers. The package can also be used to
    analyze team performances.

A finite mixture of Zero-Inflated Poisson (ZIP) models for analyzing criminal trajectories.

Gives convenient access to publicly available police-recorded open
    crime data from large cities in the United States that are included in the
    Crime Open Database <https://osf.io/zyaqn/>.

A comprehensive collection of datasets exclusively focused on crimes, criminal activities, and related topics. 
    This package serves as a valuable resource for researchers, analysts, and students interested in crime analysis, 
    criminology, social and economic studies related to criminal behavior. Datasets span global and local contexts, 
    with a mix of tabular and spatial data.

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.

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>.