Methodology for subgroup selection in the context of isotonic regression including methods for sub-Gaussian errors, classification, homoscedastic Gaussian errors and quantile regression. See the documentation of ISS(). Details can be found in the paper by Müller, Reeve, Cannings and Samworth (2023) <arXiv:2305.04852v2>.

Manage a 'GitHub' problem using R: wrangle issues, labels and
    milestones. It includes functions for storing, prioritizing (sorting),
    displaying, adding, deleting, and selecting (filtering) issues based
    on qualitative and quantitative information. Issues (labels and
    milestones) are written in lists and categorized into the S3 class to
    be easily manipulated as datasets in R.

You can access to open data published in Instituto Canario De Estadistica (ISTAC) APIs at <https://datos.canarias.es/api/estadisticas/>.

Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a 'shiny' application called 'shinyIstat' can be used to search, download and filter datasets more easily.

Launches a 'shiny' based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.

Provides functions to to compute a continuum of information-based measures 
 for quantifying the temporal stability of populations, communities, and ecosystems, 
 as well as their associated synchrony, based on species (or species assemblage)
 biomass or other key variables. When biodiversity data are available, the package
 also enables the assessment of the corresponding diversity–stability relationships.
 All measures are applicable in both temporal and spatial contexts. The theoretical
 and methodological background is detailed in Chao et al. (2025) 
 <doi:10.1101/2025.08.20.671203>.