LearnBayes contains a collection of functions helpful in
learning the basic tenets of Bayesian statistical inference.
It contains functions for summarizing basic one and two
parameter posterior distributions and predictive distributions.
It contains MCMC algorithms for summarizing posterior
distributions defined by the user.  It also contains functions
for regression models, hierarchical models, Bayesian tests, and
illustrations of Gibbs sampling.

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

c(
person("Roberto","Alcantara", role = c("aut","cre"),
email = "roberto.alcantara@edu.uah.es"),
person("Juan Jose Cuadrado", role = c("aut"),
email = "jjcg@uah.es"),
person("Universidad de Alcala de Henares", role = c("aut"))
)
Roberto Alcantara [aut, cre],
Juan Jose Cuadrado [aut],
Universidad de Alcala de Henares [aut]
Classical hierarchical clustering algorithms, agglomerative and divisive clustering. Algorithms are implemented as a theoretical way, step by step.
It includes some detailed functions that explain each step. Every function allows options to get different results using different techniques.
The package explains non expert users how hierarchical clustering algorithms work.

Functions for Learning Exploratory Data Analysis
LearnEDA contains a collection of functions helpful in learning the basic tenets of Exploratory Data Analysis.

LearnEDA contains a collection of functions helpful in
learning the basic tenets of Exploratory Data Analysis.

Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.