xts, zoo, outliers, nnet, pracma, sandwich
Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing
attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic
dynamic systems which can behave like an erratic and apparently random motion. A relevant field
inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour
from empirical time series data. One of the main features of chaos is the well known initial value
sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify
the initial value sensitive property estimating the Lyapunov exponents. The DChaos package
provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos
based on the Lyapunov exponent in order to know if the data generating process behind time series
behave chaotically or not.

Functions for the detection of spatial clusters of diseases
Virgilio Gómez-Rubio <virgil@uv.es>, Juan Ferrándiz <juan.ferrandiz@uv.es>, Antonio López <antonio.lopez@uv.es>
A set of functions for the detection of spatial
clusters of disease using count data. Bootstrap is used to estimate sampling
distributions of statistics.

Virgilio Gómez-Rubio, Juan Ferrándiz and Antonio López, with contributions by Roger Bivand.

Virgilio Gómez-Rubio, Juan Ferrándiz-Ferragud and Antonio López-Quílez, with contributions by Roger Bivand.

Virgilio Gómez-Rubio, Juan Ferrándiz-Ferragud and Antonio
López-Quílez, with contributions by Roger Bivand.
A set of functions for the detection of spatial clusters
of disease using count data. Bootstrap is used to estimate
sampling distributions of statistics.

Functions for the Detection of Spatial Clusters of Diseases

Model-Based Detection of Disease Clusters
Virgilio Gomez-Rubio, Paula Esther Moraga Serrano, Barry
Rowlingson
R (>= 2.10), parallel, sp, spacetime, DCluster
methods, xts, lme4, pscl, RColorBrewer, gridExtra,
latticeExtra
Model-based methods for the detection of disease clusters
using GLMs, GLMMs and zero-inflated models.