Tools for evaluating link prediction and clustering algorithms 
    with respect to ground truth. Includes efficient implementations of 
    common performance measures such as pairwise precision/recall, 
    cluster homogeneity/completeness, variation of information, 
    Rand index etc.

Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <DOI:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <DOI:10.1201/b12728>).

A suite of tools to build attractive command line interfaces
    ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs,
    etc. Supports custom themes via a 'CSS'-like language. It also
    contains a number of lower level 'CLI' elements: rules, boxes, trees,
    and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI
    colors and text styles as well.

Create rich command line applications, with colors, headings,
    lists, alerts, progress bars, etc. It uses CSS for custom themes.
    This package is now superseded by the 'cli' package. Please use 'cli'
    instead in new projects.

Implement 'tableGrob' object as a clickable image map.
    The 'clickableImageMap' package is designed to be more convenient and more configurable than the edit() function.
    Limitations that I have encountered with edit() are cannot control
    (1) positioning
    (2) size
    (3) appearance and formatting of fonts
    In contrast, when the table is implemented as a 'tableGrob', all of these features are controllable.
    In particular, the 'ggplot2' grid system allows exact positioning of the table relative to
    other graphics etc.

Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.