sp, trajectories, xts, spacetime, RPostgreSQL, geosphere,
methods, rgdal
testthat, knitr, rmarkdown, covr, ggplot2, rgeos, magrittr

Fit Linear Transformation Models for Censored Data
A unified estimation procedure for the analysis of censored data using linear transformation models.

Fit Linear Transformation Models for Right Censored Data
A unified estimation procedure for the analysis of right censored data using linear transformation models.

A unified estimation procedure for the analysis of right censored data using linear transformation models. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.

Implementation of Transportation Problem Algorithms
Implementation of two transportation problem algorithms.
1. North West Corner Method
2. Minimum Cost Method or Least cost method.
For more technical details about the algorithms please refer below URLs.
<http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
<http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.

Inference of Transmission Tree from a Dated Phylogeny
c(person("Xavier","Didelot",
email="xavier.didelot@gmail.com",
comment = c(ORCID = "0000-0003-1885-500X"),
role=c("aut", "cre")),
person("Yuanwei", "Xu",
email = "y.xu@bham.ac.uk",
role = c("ctb")))
Inference of transmission tree from a dated phylogeny.
Includes methods to simulate and analyse outbreaks.
The methodology is described in
Didelot et al. (2014) <doi:10.1093/molbev/msu121>,
Didelot et al. (2017) <doi:10.1093/molbev/msw275>.
knitr, testthat, purrr, coda, grDevices, lattice
Xavier Didelot [aut, cre] (<https://orcid.org/0000-0003-1885-500X>),
Yuanwei Xu [ctb]

knitr, testthat, purrr, coda, grDevices, lattice, rmarkdown

Transfer Learning for Tensor Graphical Models
c(person("Mingyang", "Ren", email = "renmingyang17@mails.ucas.ac.cn", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8061-9940")),
person("Yaoming", "Zhen", role = c("aut")),
person("Junhui", "Wang", role = c("aut")))
Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs.
Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights.
Reference:
Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <arXiv:2211.09391>.
MASS, Matrix, rTensor, Tlasso, glasso, doParallel,expm
Mingyang Ren [aut, cre] (<https://orcid.org/0000-0002-8061-9940>),
Yaoming Zhen [aut],
Junhui Wang [aut]