Dimensionality Reduction Methods in a Common Format
c(
person(
"Robrecht", "Cannoodt",
email = "rcannood@gmail.com",
role = c("aut"),
comment = c(ORCID = "0000-0003-3641-729X", github = "rcannood")
),
person(
"Wouter", "Saelens",
email = "wouter.saelens@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7114-6248", github = "zouter")
)
)
Provides a common interface for applying dimensionality reduction methods,
such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps,
Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'),
and Uniform Manifold Approximation and Projection ('UMAP').
Has built-in support for sparse matrices.
testthat, destiny, diffusionMap, fastICA, lle, MASS, Rtsne,
smacof, uwot

testthat, destiny, diffusionMap, fastICA, lle, MASS, Matrix,
Rtsne, smacof, uwot

testthat, destiny, diffusionMap, fastICA, igraph, lle, MASS,
Matrix, RANN, Rtsne, smacof, uwot

c(
person(
"Robrecht", "Cannoodt",
email = "rcannood@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-3641-729X", github = "rcannood")
),
person(
"Wouter", "Saelens",
email = "wouter.saelens@gmail.com",
role = c("aut"),
comment = c(ORCID = "0000-0002-7114-6248", github = "zouter")
)
)
dynutils (>= 1.0.5), irlba, lmds, stats, tibble
testthat, diffusionMap, fastICA, igraph, lle, MASS, Matrix,
RANN, Rtsne, smacof, uwot

Resisting Neighbor Label Attack in a Dynamic Network
An anonymization algorithm to resist neighbor label attack in a dynamic network.
igraph, doParallel, foreach, grDevices, graphics, utils,
parallel

Feature Importance for Dynamic Processes
Calculating feature importance scores from trajectories using the random forests algorithm and more. Saelens and Cannoodt et
al. (2019) <doi:10.1038/s41587-019-0071-9>.
dplyr, dynutils (>= 1.0.2), dynwrap (>= 1.0.0), purrr,
magrittr, methods, ranger, reshape2, testthat, tidyr, tibble

Fitting Dynamic Frailty Models with the EM Algorithm
Fits semiparametric dynamic frailty models according to the methodology of Putter and van Houwelingen (2015) <doi:10.1093/biostatistics/kxv002>. Intermediate models, where the frailty is piecewise constant on prespecified intervals, are also supported. The frailty process is taken to have a specific auto-correlation structure, and the supported distributions include gamma, inverse Gaussian, power variance family (PVF) and positive stable.
Rcpp (>= 0.12.8), magrittr, tibble, dplyr, tidyr
'RcppExports.R' 'dynfrail_fit.R' 'dynfrail_aux.R' 'dynfrail.R'
'dynfrail_arguments.R' 'dynfrail_prep.R' 'print.dynfrail.R'