Tools for the analysis of psychophysical data in R. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012).

Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.

Implements seven estimators for average treatment effect (ATE)
    estimation in mixed-subjects designs (MSDs), where human subjects data is
    augmented with predictions from large language models (LLMs). Includes
    Difference-in-Means, GREG, PPI++, Doubly-Tuned, Difference-in-Predictions
    (DiP), DiP++, and D-T DiP estimators. Provides point estimates, variance
    estimation via delta-method or bootstrap, and optimal design selection for
    budget allocation between human observations and LLM predictions.

Integrates large language model generated item responses into
    psychometric calibration studies through a mixed-subjects design for
    unidimensional two-parameter and one-parameter logistic item response
    theory models. Human pilot responses are augmented with model-generated
    responses using a prediction-powered inference estimator (Angelopoulos,
    Bates, Fannjiang, Jordan and Zrnic (2023) <doi:10.1126/science.adi6000>;
    Angelopoulos, Duchi and Zrnic (2023) <doi:10.48550/arXiv.2311.01453>)
    adapted to marginal maximum-likelihood estimation, following the
    mixed-subjects design of Broska, Howes and van Loon (2025)
    <doi:10.1177/00491241251326865>. The estimator is anchored to the human
    responses and is asymptotically unbiased for the human item parameters at
    any tuning weight; the weight on the synthetic responses is chosen to
    minimize propagated ability-score risk, down-weighting uninformative or
    biased generated responses. Louis-corrected sandwich standard errors,
    ability scoring, cross-fitted tuning, and scale linking are also provided.