A Gaussian or Student's t copula-based procedure for generating samples from discrete random variables with prescribed correlation matrix and marginal distributions.

Genotyping of triploid individuals from luminescence data (marker probeset A and B). Works also for diploids and tetraploids.
	Three main functions: Create_Dataset_from_file() to build dataset ; Clustering_parallele_from_dir() that regroups individuals with a same genotype based on proximity and
	Genotyping_parallele_from_dir() that assigns a genotype to each cluster. For Shiny interface use: launch_GenoShiny().

Sequential change-point tests, parameters estimation, and goodness-of-fit tests for generalized Ornstein-Uhlenbeck processes.

It provides an interesting solution for handling a high number 
	of segmentation variables in partial least squares structural equation 
	modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez, 
	and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient 
	test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>) 
	to detect the path coefficients responsible for the identified differences). 
	The package also allows running the hybrid multi-group approach (Lamberti (2021) 
	<doi:10.1007/s11135-021-01096-9>).

Turns one-off iterative R procedures (such as for loops,
    lapply() or pmap() from 'purrr') into production-grade workflows by
    wrapping them with orthogonal, composable execution layers. Two layers
    are always active: structured logging with real traceback and per-case
    timing; and reproducibility capture, which records the R version,
    loaded package versions, execution environment, the exact iteration
    mask, and a stat-based fingerprint of every input file referenced in
    the mask (with a diff_inputs() helper to detect silent drift between
    runs). Parallel execution (built on the 'future' framework, Bengtsson
    (2021) <doi:10.32614/RJ-2021-048>), non-blocking background jobs, and
    opt-in progress reporting (via 'progressr') are implemented as
    optional, composable layers. Further layers (error replay,
    content-hash input fingerprinting, content-based case identifiers) are
    planned and will remain composable with the default layers.