Tukeys Trend Test via Multiple Marginal Models (tukeytrend)
Provides wrapper functions to the multiple marginal model function mmm() of package ‘multcomp’ to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.

Optimal Segmentation Subject to Up-Down Constraints (PeakSegOptimal)
Computes optimal changepoint models using the Poisson likelihood for non-negative count data, subject to the PeakSeg constraint: the first change must be up, second change down, third change up, etc. For more info about the models and algorithms, read ‘A log-linear time algorithm for constrained changepoint detection’ <arXiv:1703.03352> by TD Hocking et al.

Annotate Statistical Tests for ‘ggplot2’ (ggpval)
Automatically perform desired statistical tests (e.g. wilcox.test(), t.test()) to compare between groups, and add test p-values to the plot with annotation bar. Visualizing group differences are frequently performed by boxplots, violin plots etc.. Statistical test results are often needed to be annotated on the plots. This package provide a convenient function that work on ‘ggplot2’ objects, perform desired statistical test between groups of interest and annotate the test results on the plot.

Tidy Output from Regular Expression Matching (rematch2)
Wrappers on ‘regexpr’ and ‘gregexpr’ to return the match results in tidy data frames.

Reduced-Rank Regression (rrpack)
Multivariate regression methodologies including reduced-rank regression (RRR), reduced-rank ridge regression (RRS), robust reduced-rank regression (R4), generalized/mixed-response reduced-rank regression (mRRR), row-sparse reduced-rank regression (SRRR), reduced-rank regression with a sparse singular value decomposition (RSSVD), and sparse and orthogonal factor regression (SOFAR).