Performs the MRFA approach proposed by Sung et al. (2017+) <arXiv:1709.07064> to fit
and predict nonlinear regression problems, particularly for large-scale and
high-dimensional problems. The application includes deterministic or stochastic
computer experiments, spatial datasets, and so on.

Performs the MRFA approach proposed by Sung et al. (2019+) <arXiv:1709.07064> to fit
and predict nonlinear regression problems, particularly for large-scale and
high-dimensional problems. The application includes deterministic or stochastic
computer experiments, spatial datasets, and so on.

Markov Random Fields with Additional Covariates
c(
person("Nicholas J", "Clark", email = "nicholas.j.clark1214@gmail.com", role = c("aut", "cre")),
person("Konstans", "Wells", email = "konswells@gmail.com", role = "aut"),
person("Oscar", "Lindberg", email = "o.lindberg@gmail.com", role = "aut"))
Approximate node interaction parameters of Markov Random Fields
graphical networks. Models can incorporate additional covariates, allowing users to estimate
how interactions between nodes in the graph are predicted to change across
covariate gradients. The general methods implemented in this package are described
in Clark et al. (2018) <doi:10.1002/ecy.2221>.
purrr, parallel, plyr, ggplot2, dplyr, caret, gridExtra,
magrittr, Matrix, stats, utils, reshape2, sfsmisc, igraph,
grDevices, pbapply, mgcv
Nicholas J Clark [aut, cre],
Konstans Wells [aut],
Oscar Lindberg [aut]

purrr, parallel, plyr, ggplot2, dplyr, caret, gridExtra,
magrittr, Matrix, stats, utils, reshape2, sfsmisc, igraph,
grDevices, pbapply, mgcv, MASS

Multi-Resolution Estimation of the Hazard Rate
Yolanda Hagar, Yuanting Chen, Vanja Dukic
Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times.

Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up.