Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; in version 2.3.0 improved update method. Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.

Treatments of a one-way layout, being equivalent to a control, 
 can be selected with this package. Bonferroni adjusted "two one-sided 
 t-tests" (TOST) and related simultaneous confidence intervals are given for 
 both differences or ratios of means of normally distributed data. For the 
 case of equal variances and balanced sample sizes for the treatment groups, 
 the single-step procedure of Bofinger and Bofinger (1995) 
 <doi:10.1111/j.2517-6161.1995.tb02058.x> can be chosen. For non-normal data, 
 the Wilcoxon test is applied.

Compute common data quality metrics for accuracy, precision and data loss
  for screen-based eye trackers. The package supports gaze input in screen pixels or
  degrees and reports angular measures in degrees where appropriate. If you use this
  package, please cite Niehorster, D.C., Nyström, M., Hessels, R.S., Benjamins, J.S.,
  Andersson, R., and Hooge, I.T.C. (2026). The fundamentals of eye tracking, Part 7:
  Determining data quality. Behavior Research Methods. <doi:10.3758/s13428-026-03039-4>.

A robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. 
    It is designed to be compatible with the 'tidyverse' data workflow, including plotting with 'ggplot2'.
    It ensures lightning-fast computations by integrating high-performance 'C++' code through 'Rcpp' package.

Tools for quantifying Traditional Ecological Knowledge (TEK), modeling TEK in decision frameworks, and designing structured decision-framing exercises in conservation and development contexts. The package implements quantitative ethnobotany indices (Use Value, Relative Frequency of Citation, etc.) but positions them within a larger framework of Bayesian modeling and participatory decision analysis. Includes critical assessment of indices' limitations and case studies of participatory workshops.