Semi-Supervised Classification, Regression and Clustering
Methods
Providing a collection of techniques for semi-supervised
classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
data are used to train a classifier. The package includes a collection of
semi-supervised learning techniques: self-training, co-training, democratic,
decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface
that is easy to use.
stats, parsnip, plyr, dplyr (>= 0.8.0.1), magrittr, purrr,
rlang (>= 0.3.1), proxy, methods, generics, utils, RANN,
foreach, RSSL, conclust
caret, tidymodels, e1071, C50, kernlab, testthat, doParallel,
tidyverse, factoextra, survival, covr, kknn, randomForest,
ranger, MASS, nlme, knitr, rmarkdown

caret, tidymodels, e1071, C50, kernlab, testthat, doParallel,
tidyverse, factoextra, survival, xgboost, covr, kknn,
randomForest, ranger, MASS, nlme, knitr, rmarkdown

Fit and Analyze Smooth Supersaturated Models
person("Peter","Curtis", email = "peterrobertcurtis@gmail.com",
role = c("aut", "cre"))
Creates an S4 class "SSM" and defines functions for fitting smooth
supersaturated models, a polynomial model with spline-like behaviour.
Functions are defined for the computation of Sobol indices for sensitivity
analysis and plotting the main effects using FANOVA methods. It also
implements the estimation of the SSM metamodel error using a GP model with
a variety of defined correlation functions.

MASS, BH, igraph (>= 0.6), maptools, lattice, sp, methods
Geostatistical modeling for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including covariates, can be fit with ML or REML.  Mapping and other graphical functions are included.

MASS, igraph (>= 1.0.0), maptools, lattice, methods, Matrix,
rgdal (>= 1.2-5), rgeos (>= 0.3-22)
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.