dplyr, glue, janitor (>= 1.0.0), lubridate, magrittr, readr,
readxl, rlang, stringr, tibble

Concept Drift and Concept Shift Detection for Predictive Models
Concept drift refers to the change in the data distribution or
in the relationships between variables over time.
'drifter' calculates distances between variable distributions or
variable relations and identifies both types of drift.
Key functions are:
calculate_covariate_drift() checks distance between corresponding variables in two datasets,
calculate_residuals_drift() checks distance between residual distributions for two models,
calculate_model_drift() checks distance between partial dependency profiles for two models,
check_drift() executes all checks against drift.
'drifter' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.

Distinguishing drift and natural selection as causes of
phenotypic differentiation
R (>= 2.12.0), MCMCpack, SparseM, corpcor, car

R (>= 2.12.0), MCMCpack, SparseM, corpcor

Estimation, Simulation and Reliability of Drifting Markov Models
c(person("Vlad Stefan", "Barbu", role = "aut",
email = "vladstefan.barbu@univ-rouen.fr"),
person("Geoffray", "Brelurut", role = "ctb"),
person("Annthomy", "Gilles", role = "ctb"),
person("Arnaud", "Lefebvre", role = "ctb"),
person("Corentin", "Lothode", role = "aut",
email = "corentin.lothode@univ-rouen.fr"),
person("Victor", "Mataigne", role = "ctb"),
person("Alexandre", "Seiller", role = "aut"),
person("Nicolas", "Vergne", role = c("aut","cre"),
email = "nicolas.vergne@univ-rouen.fr"))
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), <doi:10.2202/1544-6115.1326> and
Barbu, V.S., Vergne, N. (2019) <doi:10.1007/s11009-018-9682-8> .
seqinr, Biostrings, testthat, stats, ggplot2, parallel,
future, doParallel, foreach, tidyverse, dplyr, reshape2, Rdpack
Vlad Stefan Barbu [aut],
Geoffray Brelurut [ctb],
Annthomy Gilles [ctb],
Arnaud Lefebvre [ctb],
Corentin Lothode [aut],
Victor Mataigne [ctb],
Alexandre Seiller [aut],
Nicolas Vergne [aut, cre]