Generates experiments - simulating structured or experimental data as: 
             completely randomized design, randomized block design, latin square design, 
             factorial and split-plot experiments (Ferreira, 2008, ISBN:8587692526; 
             Naes et al., 2007 <doi:10.1002/qre.841>; Rencher et al., 2007, ISBN:9780471754985; 
             Montgomery, 2001, ISBN:0471316490).

Download geyser eruption and observation data from the GeyserTimes
  site (<https://geysertimes.org>) and optionally store it locally. The vignette
  shows a simple analysis of downloading, accessing, and summarizing the data.

Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling, and it has been presented originally by Virtanen et al. (2012), and extended in Klami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte et al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details, see the citation info.

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL '<https://oops.uni-oldenburg.de/id/eprint/4290>').

Supports analyses using the Global Forest Change dataset released
    by Hansen et al. gfcanalysis was originally written for the Tropical Ecology 
    Assessment and Monitoring (TEAM) Network. For additional details on the 
    Global Forest Change dataset, see: Hansen, M. et al. 2013. "High-Resolution 
    Global Maps of 21st-Century Forest Cover Change." Science 342 (15 
    November): 850-53. The forest change data and more information on the 
    product is available at <http://earthenginepartners.appspot.com>.

Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
    Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.