Tools for maximum likelihood estimation of parameters 
  of scientific models. Based on Goffe et al (1994) <doi:10.1016/0304-4076(94)90038-8>.

Constructs likelihood models from heterogeneous observation types
    by composing named contributions. Each observation type (exact, left-censored,
    right-censored, interval-censored, or custom) contributes independently to the
    total log-likelihood, which is summed under an i.i.d. assumption. Provides
    contr_name() for standard R distributions and contr_fn() for user-defined
    contributions, composed via likelihood_contr() into objects compatible with
    the likelihood.model inference framework.

Facilitates building likelihood models in the Fisherian tradition
    following Richard Royall (1997, ISBN:978-0412044113) "Statistical Evidence: A
    Likelihood Paradigm". Defines generic methods for working with likelihoods
    (loglik(), score(), hess_loglik(), fim()) and provides functions for pure
    likelihood-based inference (support(), relative_likelihood(),
    likelihood_interval(), profile_loglik()).

Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.

Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.

A collection of functions that calculate the log likelihood 
    (support) for a range of statistical tests. Where possible the likelihood 
    function and likelihood interval for the observed data are displayed. The 
    evidential approach used here is based on the book "Likelihood" by A.W.F. 
    Edwards (1992, ISBN-13 : 978-0801844430), "Statistical Evidence" by R. 
    Royall (1997, ISBN-13 : 978-0412044113), S.N. Goodman & R. Royall 
    (2011) <doi:10.2105/AJPH.78.12.1568>, "Understanding 
    Psychology as a Science" by Z. Dienes (2008, ISBN-13 : 978-0230542310), 
    S. Glover & P. Dixon <doi:10.3758/BF03196706> 
    and others. This package accompanies "Evidence-Based Statistics" by 
    P. Cahusac (2020, ISBN-13 : 978-1119549802) 
    <doi:10.1002/9781119549833>.