Estimation, Simulation and Reliability of Drifting Markov Models • drimmR

Description

Performs the drifting Markov models (DMM) which are non-homogeneous Markov models designed for modeling the heterogeneities of sequences in a more flexible way than homogeneous Markov chains or even hidden Markov models. In this context, we developed an R package dedicated to the estimation, simulation and the exact computation of associated reliability of drifting Markov models. The implemented methods are described in:

Vergne, N. (2008). Drifting Markov models with polynomial drift and applications to DNA sequences. Statistical applications in genetics and molecular biology, 7(1). doi:10.2202/1544-6115.1326. Journal version
Barbu, V. S., & Vergne, N. (2019). Reliability and survival analysis for drifting Markov models: modeling and estimation. Methodology and Computing in Applied Probability, 21(4), 1407-1429. doi:10.1007/s11009-018-9682-8. Journal version

Contribute

The official repository is at PLMLab. But to help with issues and contributions, a mirror has been setup at Github.

Install

Install from CRAN:

install.packages('drimmR')

Install latest development version from git:

if (!require("devtools")) {
  install.packages("devtools")
}
devtools::install_git("https://plmlab.math.cnrs.fr/lmrs/statistique/drimmR", dependencies = TRUE, build_vignettes = FALSE)

Getting started

library("drimmR")
data(lambda, package = "drimmR")
dmm <- fitdmm(lambda, 1, 1, c('a','c','g','t'), init.estim = "freq", fit.method="sum")
simulate(dmm, model_size=100)

Parallelisation

You can use multiple processors for most functions. Add ncpu=4 to run on 4 cores. If you want R to detect the number of available cores on your system, use ncpu=-1.

License

GPL v3