Title: A Tool for 'Covariate'-Sensitive Longitudinal Analysis on
for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input,
p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

Authors@R: 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"))
Description: 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.
Imports: Rcpp, stats, longmemo, partitions, fracdiff, mvtnorm
Packaged: 2019-02-09 13:31:27 UTC; Christian
Author: Christian Leschinski [aut, cre],

Title: Random Forests for Longitudinal Data
email = "Louis.capitaine@u-bordeaux.fr",comment = c(ORCID = "0000-0001-6800-2342"))
Description: 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.
Imports: stats, randomForest, rpart, mvtnorm, latex2exp
Packaged: 2020-08-25 22:24:05 UTC; capitainelouis
Author: Louis Capitaine [aut, cre] (<https://orcid.org/0000-0001-6800-2342>)

Title: A collection of tools to conduct Levins' Loop Analysis
Description: Loop analysis provides qualitative predictions of paramter change in a system of causally interdependent elements, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (graphviz.org). Loop Analyst provides tools for the construction and output of community matrices, computation and output of community effect matrices, and feedback, path and loop enumeration tools.
Packaged: Tue Feb  7 11:13:20 2006; alexis