Title: Clustering a Dataset using Multi-SOM Algorithm
Description: Implements two version of Multi-SOM algorithm namely stochastic Multi-SOM and batch Multi-SOM. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.

Title: Clustering a Data Set using Multi-SOM Algorithm
Description: Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.

Title: Multispatial Convergent Cross Mapping
Description: The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.

Author: Yohann Foucher, Florence Gillaizeau
Description: Medical researchers are often interested in investigating the relationship between explicative variables and multiple times-to-event. Time-inhomogeneous Markov models consist of modelling the probabilities of transitions according to the chronological times (times since the baseline of the study). Semi-Markov (SM) models consist of modelling the probabilities of transitions according to the times spent in states. In this package, we propose functions implementing such 3-state and 4-state multivariable and multistate models. The user can introduce multiple covariates to estimate conditional (subject-specific) effects. We also propose to adjust for possible confounding factors by using the Inverse Probability Weighting (IPW). When a state is patient death, the user can consider to take into account the mortality of the general population (relative survival approach). Finally, in the particular situation of one initial transient state and two competing and absorbing states, this package allows for estimating mixture models.
Depends: R (>= 2.10), survival, statmod, date, relsurv