A Tool for 'Covariate'-Sensitive Longitudinal Analysis on
'omics' Data
This tool takes longitudinal dataset as input and analyzes if there is significant
change of the features over time (a proxy for treatments), while detects and controls
for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input,
including count, proportion, binary, ordinal and continuous data. The output table contains
p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

c(person("Christian", "Leschinski", email="christian_leschinski@gmx.de", role=c("aut", "cre")), person("Michelle", "Voges", email="voges@statistik.uni-hannover.de", role="ctb"), person("Kai", "Wenger", email="wenger@statistik.uni-hannover.de", role="ctb"))
Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.
Rcpp, stats, longmemo, partitions, fracdiff, mvtnorm
Christian Leschinski [aut, cre],
Michelle Voges [ctb],
Kai Wenger [ctb]

person(given = "Louis",
family = "Capitaine",
role = c("aut", "cre"),
email = "Louis.capitaine@u-bordeaux.fr",comment = c(ORCID = "0000-0001-6800-2342"))
Random forests are a statistical learning method widely used in many areas of scientific research essentially for its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data. However, current random forests approaches are not flexible enough to handle longitudinal data.  In this package, we propose a general approach of random forests for high-dimensional longitudinal data. It includes a flexible stochastic model which allows the covariance structure to vary over time. Furthermore, we introduce a new method which takes intra-individual covariance into consideration to build random forests. The method is fully detailled in Capitaine et.al. (2020) <doi:10.1177/0962280220946080> Random forests for high-dimensional longitudinal data.
stats, randomForest, rpart, mvtnorm, latex2exp
Louis Capitaine [aut, cre] (<https://orcid.org/0000-0001-6800-2342>)