Three methods are implemented in R to facilitate the aggregations of flags in official statistics.  
            From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight
            is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: 
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>,
            <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>,
            <http://ec.europa.eu/eurostat/data/database/information>,
            <http://www.oecd.org/sdd/33869551.pdf>,
            <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.

Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2016). Fused Lasso Additive Model. Journal of Computational and Graphical Statistics, 25(4): 1005-1025.

Efficient implementations of the algorithms in the 
    Almost-Matching-Exactly framework for interpretable matching in causal
    inference. These algorithms match units via a learned, weighted Hamming
    distance that determines which covariates are more important to match on.
    For more information and examples, see the Almost-Matching-Exactly website.

A generative art system for producing tree-like
    images using an L-system to create the structures. The package 
    includes tools for generating the data structures and visualise
    them in a variety of styles.

Tools for fluctuations analysis of mutant cells counts. Main reference is A. Mazoyer, R. Drouilhet, S. Despreaux and B. Ycart (2017)  <doi:10.32614/RJ-2017-029>.

A set of methods to simulate from and fit computational models of
    attentional selectivity. The package implements the dual-stage two-phase
    (DSTP) model of Hübner et al. (2010) <doi:10.1037/a0019471>, and the
    shrinking spotlight (SSP) model of White et al. (2011)
    <doi:10.1016/j.cogpsych.2011.08.001>.

The Forecast Linear Augmented Projection (flap) method reduces 
    forecast variance by adjusting the forecasts of multivariate time series to 
    be consistent with the forecasts of linear combinations (components) of the 
    series by projecting all forecasts onto the space where the linear 
    constraints are satisfied. The forecast variance can be reduced 
    monotonically by including more components. For a given number of 
    components, the flap method achieves maximum forecast variance reduction 
    among linear projections.