Linear model calculations are made for many random versions of data.  
    Using residual randomization in a permutation procedure, sums of squares are 
    calculated over many permutations to generate empirical probability distributions 
    for evaluating model effects.  This packaged is described by 
    Collyer & Adams (2018).  Additionally, coefficients, statistics, fitted values, and residuals generated over many 
    permutations can be used for various procedures including pairwise tests, prediction, classification, and
    model comparison.  This package should provide most tools one could need for the analysis of
    high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

Reduced-rank regression, diagnostics and graphics.

Univariate and multivariate methods to analyze randomized response 
    (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A 
    survey technique for eliminating evasive answer bias. Journal of the 
    American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). 
    Besides univariate estimates of true proportions, RR variables can be used 
    for correlations, as dependent variable in a logistic regression (with or 
    without random effects), or as predictors in a linear regression
    (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and 
    regression analyses of randomized response data. Journal of Statistical 
    Software, 85(2), 1–29, <doi:10.18637/jss.v085.i02>). For simulations and 
    the estimation of statistical power, RR data can be generated according to 
    several models. The implemented methods also allow to test the link between 
    continuous covariates and dishonesty in cheating paradigms such as the 
    coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). 
    The statistical analysis of cheating paradigms. Behavior Research Methods, 
    49, 724–732, <doi:10.3758/s13428-016-0729-x>).

Methods for estimating online robust reduced-rank regression. 
    The Gaussian maximum likelihood estimation method is described in Johansen, S. (1991) <doi:10.2307/2938278>.
    The majorisation-minimisation estimation method is partly described in Zhao, Z., & Palomar, D. P. (2017) <doi:10.1109/GlobalSIP.2017.8309093>.
    The description of the generic stochastic successive upper-bound minimisation method
    and the sample average approximation can be found in Razaviyayn, M., Sanjabi, M., & Luo, Z. Q. (2016) <doi:10.1007/s10107-016-1021-7>.