Implements modeling and computational tools for conditional
    random fields (CRF) model as well as other probabilistic undirected
    graphical models of discrete data with pairwise and unary potentials.

Wraps the 'CRFsuite' library <https://github.com/chokkan/crfsuite> allowing users 
    to fit a Conditional Random Field model and to apply it on existing data.
    The focus of the implementation is in the area of Natural Language Processing where this R package allows you to easily build and apply models 
    for named entity recognition, text chunking, part of speech tagging, intent recognition or classification of any category you have in mind. Next to training, a small web application
    is included in the package to allow you to easily construct training data.

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 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>.