Rcpp (>= 0.11.0), mvQuad, numDeriv, randtoolbox, Formula,
stats

Rcpp (>= 0.11.0), mvQuad, numDeriv, randtoolbox, Formula,
stats , Matrix

Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse
Model
Due to a limited availability of observed high-resolution precipitation records with adequate length, simulations with stochastic precipitation models are used to generate series for subsequent studies [e.g. Khaliq and Cunmae, 1996, <doi:10.1016/0022-1694(95)02894-3>, Vandenberghe et al., 2011, <doi:10.1029/2009WR008388>]. This package contains an R implementation of the original Bartlett-Lewis rectangular pulse model (BLRPM), developed by Rodriguez-Iturbe et al. (1987) <doi:10.1098/rspa.1987.0039>. It contains a function for simulating a precipitation time series based on storms and cells generated by the model with given or estimated model parameters. Additionally BLRPM parameters can be estimated from a given or simulated precipitation time series. The model simulations can be plotted in a three-layer plot including an overview of generated storms and cells by the model (which can also be plotted individually), a continuous step-function and a discrete precipitation time series at a chosen aggregation level.

Interactive Document for Working with Binary Logistic Regression
Analysis
An interactive document on  the topic of binary logistic regression  analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://analyticmodels.shinyapps.io/BinaryLogisticRegressionModelling/>.

c(
person("Alberto", "Donizetti", email = "albe.donizetti@gmail.com", role = c("aut", "cre")),
person("Francesca", "Ieva", email = "francesca.ieva@polimi.it", role = "ctb"))
Provides a Bayesian latent space
model for complex networks, either weighted or unweighted.
Given an observed input graph, the estimates for the latent coordinates
of the nodes are obtained through a Bayesian MCMC algorithm.
The overall likelihood of the graph depends on a fundamental probability
equation, which is defined so that ties are more likely to exist
between nodes whose latent space coordinates are close.
The package is mainly based on the model by Hoff, Raftery and Handcock (2002)
<doi:10.1198/016214502388618906> and contains some extra features
(e.g., removal of the Procrustean step, weights implemented as
coefficients of the latent distances, 3D plots).
The original code related to the above model was retrieved from
<https://www.stat.washington.edu/people/pdhoff/Code/hoff_raftery_handcock_2002_jasa/>.
Users can inspect the MCMC simulation, create and customize insightful
graphical representations or apply clustering techniques.
Alberto Donizetti [aut, cre],
Francesca Ieva [ctb]