Provides functions for calculating and analyzing the proliferative 
    index (PI) from an RNA-seq dataset. As described in Ramaker & Lasseigne, 
    et al. bioRxiv, 2016 <doi:10.1101/063057>.

A user-friendly interface for creating and managing empirical crowd-sourcing studies via API access to <https://www.prolific.co>.

Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.

Estimation for blinding bias in randomized controlled trials with
  a latent continuous outcome, a binary response depending on treatment and the
  latent outcome, and a noisy surrogate subject to possibly response-dependent
  measurement error. Implements EM estimators in R backed by compiled C routines
  for models with and without the restriction delta0 = 0, and Bayesian Stan
  wrappers for the same two models. Functions were added for latent outcome
  models with differential measurement error.

Functions which can be used to support the Multicriteria Decision Analysis (MCDA) process 
    involving multiple criteria, by PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations).

The PROMETHEE method is a multi-criteria decision-making method addressing with outranking problems.
            The method establishes a preference structure between the alternatives, having a preference function for
            each criterion. IN this context, three variants of the method is carried out: PROMETHEE I (Partial Outranking),
            PROMETHEE II (Total Outranking), and PROMETHEE III (Outranking by Intervals).

PROMETHEE (Preference Ranking Organisation METHod for Enrichment of
    Evaluations) based method assesses alternatives to obtain partial and complete
    rankings. The package also provides the GLNF (Global Local Net Flow) sorting
    algorithm to classify alternatives into ordered categories, as well as an index
    function to measure the classification quality. Barrera, F., Segura, M., &
    Maroto, C. (2023) <doi:10.1111/itor.13288>. Brans, J.P.; De Smet, Y., (2016) 
    <doi:10.1007/978-1-4939-3094-4_6>.