Implements an S4 distribution system and estimation methods for
    parameters of common distribution families. The common d, p, q, r function
    family for each distribution is enriched with the ll, e, and v counterparts,
    computing the log-likelihood, performing estimation, and calculating the
    asymptotic variance - covariance matrix, respectively. Parameter estimation
    is performed analytically whenever possible.

Automatic disaggregation of small-area population estimates by demographic groups (e.g., age, sex, race, marital status, educational level, etc) along with the estimates of uncertainty, using advanced Bayesian statistical modelling approaches based on integrated nested Laplace approximation (INLA) Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> and stochastic partial differential equation (SPDE) methods Lindgren et al. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>. The package implements hierarchical Bayesian modeling frameworks for small area estimation as described in Leasure et al. (2020) <doi:10.1073/pnas.1913050117> and Nnanatu et al. (2025) <doi:10.1038/s41467-025-59862-4>.

Similarly to package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi:10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.

Functions and data to reproduce all plots in the book "Practical Smoothing. 
 The Joys of P-splines" by Paul H.C. Eilers and Brian D. Marx (2021, ISBN:978-1108482950).

A Jordan algebra is an algebraic object originally
   designed to study observables in quantum mechanics.  Jordan
   algebras are commutative but non-associative; they satisfy the
   Jordan identity.  The package follows the ideas and notation of
   K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan
   Algebras".  To cite the package in publications, please use
   Hankin (2023) <doi:10.48550/arXiv.2303.06062>.