c(
person("Michael", "Collyer", , "mlcollyer@gmail.com", c("aut", "cre"),
comment = c(ORCID = "0000-0003-0238-2201")),
person("Dean", "Adams", , role = "aut",
comment = c(ORCID = "0000-0001-9172-7894"))
)
Rcpp (>= 1.0.6), parallel, ape, ggplot2, Matrix
knitr, rmarkdown, testthat (>= 3.0.0), dplyr, tibble

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.

Online Robust Reduced-Rank Regression Estimation
c(
person("Yangzhuoran", "Yang", email = "Fin.Yang@monash.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1232-8017")),
person("Ziping", "Zhao", email = "ZhaoZiping@shanghaitech.edu.cn", role = c("aut"), comment = c(ORCID = "0000-0002-8668-6263"))
)
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>.
matrixcalc, expm, ggplot2, graphics, magrittr, mvtnorm, stats

matrixcalc, expm, ggplot2, magrittr, mvtnorm, stats

Randomized Response Techniques for Complex Surveys
Beatriz Cobo Rodríguez, María del Mar Rueda García, Antonio Arcos
Cebrián
Point and interval estimation of linear parameters with data
obtained from complex surveys (including stratified and clustered samples)
when randomization techniques are used. The randomized response technique
was developed to obtain estimates that are more valid when studying
sensitive topics. Estimators and variances for 14 randomized response
methods for qualitative variables and 7 randomized response methods for
quantitative variables are also implemented. In addition, some data sets
from surveys with these randomization methods are included in the package.