Methods and functions for fitting maximum likelihood models in R. This package modifies and extends the 'mle' classes in the 'stats4' package.

A system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) <doi:10.12688/f1000research.5981.1>.

Features highly configurable search spaces via the 'paradox'
    package and optimizes every user-defined objective function. The
    package includes several optimization algorithms e.g. Random Search,
    Iterated Racing, Bayesian Optimization (in 'mlr3mbo') and Hyperband
    (in 'mlr3hyperband'). bbotk is the base package of 'mlr3tuning',
    'mlr3fselect' and 'miesmuschel'.

Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.

Provides comprehensive tools for blinded sample size re-estimation (BSSR) 
    in two-arm clinical trials with binary endpoints. Unlike traditional fixed-sample 
    designs, BSSR allows adaptive sample size adjustments during trials while 
    maintaining statistical integrity and study blinding. Implements five exact 
    statistical tests: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled 
    exact unconditional, and Boschloo exact unconditional tests. Supports restricted, 
    unrestricted, and weighted BSSR approaches with exact Type I error control.
    Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051> 
    and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.

The blocked weighted bootstrap (BBW) is an estimation technique 
    for use with data from two-stage cluster sampled surveys in which either 
    prior weighting (e.g. population-proportional sampling or PPS as used in 
    Standardized Monitoring and Assessment of Relief and Transitions or SMART 
    surveys) or posterior weighting (e.g. as used in rapid assessment method or
    RAM and simple spatial sampling method or S3M surveys) is implemented. See 
    Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of 
    bootstrap to cluster samples. See Aaron et al (2016) 
    <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) 
    <doi:10.1371/journal.pone.0162462> for application of the blocked weighted 
    bootstrap to estimate indicators from two-stage cluster sampled surveys.