Provides tools for sparse regression modelling with grouped predictors using the group
subset selection penalty. Uses coordinate descent and local search algorithms to rapidly deliver
near optimal estimates. The group subset penalty can be combined with a group lasso or ridge
penalty for added shrinkage. Linear and logistic regression are supported, as are overlapping
groups.

Group Sequential Analysis of Clinical Trials
Design of group sequential trials, including non-binding futility analysis
at multiple time points (Gallo, Mao, and Shih, 2014, <doi:10.1080/10543406.2014.932285>).

Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.

Alternative Implementations of Base R Functions
Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.

Alternate Implementations of Base R Functions
Alternate implementations of some base R functions, including sort, order, and match.  Functions are faster and/or have been otherwise augmented.

R (>= 2.10.1), grDevices, graphics, stats, utils, MASS, rgl,
misc3d
The original Matlab toolbox by Leola A. Alfonso-Reese. R port,
with several significant modifications by Kazunaga Matsuki.
Functions to generate and analyze data for psychology
experiments based on the General Recognition Theory.

Functions to generate and analyze data for psychology experiments based on the General Recognition Theory.
R (>= 3.2.0), grDevices, graphics, stats, utils, MASS

River Hydrograph Separation and Analysis
c(
person("Timofey", "Samsonov",, "iamste@yandex.ru", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-5994-0302")
),
person("Ekaterina", "Retz", role = "ctb", comment = c(ORCID = "0000-0002-4505-1173")),
person("Maria", "Kireeva", role = "ctb", comment = c(ORCID = "0000-0002-8285-9761"))
)
River hydrograph separation and daily runoff time series analysis. Provides
various filters to separate baseflow and quickflow using methods by
Lyne and Hollick (1979) <https://www.researchgate.net/publication/272491803_Stochastic_Time-Variable_Rainfall-Runoff_Modeling>,
Chapman (1991) <doi:10.1029/91WR01007>,
Boughton (1993) <https://cir.nii.ac.jp/crid/1572543026556977024>,
Jakeman and Hornberger (1993) <doi:10.1029/93WR00877>,
Chapman and Maxwell (1996) <https://search.informit.org/doi/10.3316/informit.360361071346753>,
and Kudelin (1960) <https://www.worldcat.org/title/printsipy-regionalnoi-otsenki-estestvennykh-resursov-podzemnykh-vod/>.
Implements advanced separation technique by Rets et al. (2022) <doi:10.1134/S0097807822010146>
which involves meteorological data to reveal genetic components of the runoff:
ground, rain, thaw and spring (seasonal thaw). High-performance C++17 computation,
annually aggregated variables, statistical testing and numerous plotting functions
for high-quality visualization.
crayon, progress, Rcpp, magrittr, dplyr, tidyr, lubridate,
stringr, rlang, grid, ggplot2, knitr, rmarkdown, kableExtra,
zoo, trend, mblm
ggridges, ggHoriPlot, ggthemes, ncdf4, sf, testthat, stringi