Title: Secondary Structure Plotting for RNA
Description: Functions for creating and manipulating RNA secondary structure plots.

Title: Linear Model Evaluation with Randomized Residuals in a
Description: 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 method is described by

Collyer, Sekora, & Adams (2015) <doi:10.1038/hdy.2014.75>.  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.

for evaluating model effects.  This packaged is described by
Collyer & Adams (2018) <doi:10.1111/2041-210X.13029>.  Additionally, coefficients, statistics, fitted values, and residuals generated over many

person("Michael", "Collyer", , "mlcollyer@gmail.com", c("aut", "cre")),

person("Michael", "Collyer", , "mlcollyer@gmail.com", c("aut", "cre"),
Imports: Rcpp (>= 1.0.6), parallel, ape, ggplot2, Matrix
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), dplyr, tibble
Author: Michael Collyer [aut, cre] (<https://orcid.org/0000-0003-0238-2201>),

Collyer & Adams (2018).  Additionally, coefficients, statistics, fitted values, and residuals generated over many

Title: Online Robust Reduced-Rank Regression Estimation
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"))
Description: 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>.
Imports: matrixcalc, expm, ggplot2, graphics, magrittr, mvtnorm, stats
Author: Yangzhuoran Yang [aut, cre] (<https://orcid.org/0000-0002-1232-8017>),