magic, trust, VGAM, Matrix, graphics, grDevices, stats, utils

Semi-Parametric Sample Selection Modelling with Continuous or
Discrete Response
Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.
magic, trust, VGAM, Matrix, graphics, grDevices, stats, utils,
VineCopula, matrixStats

magic, trust, VGAM, Matrix, graphics, grDevices, stats, utils,
CDVine, matrixStats

Implements several safe graph-based semi-supervised learning algorithms. The first algorithm is the Semi-Supervised Semi-Parametric Model (S4PM) and the fast Anchor Graph version of this approach.  For additional technical details, refer to Culp and Ryan (2013) <http://jmlr.org/papers/v14/culp13a.html>, Ryan and Culp (2015) <http://www.jmlr.org/papers/v16/ryan15a.html> and the package vignette. The underlying fitting routines are executed in C++. All tuning parameter estimation is optimized using K-fold Cross-Validation.

Collection of datasets from Sen & Srivastava:
Regression Analysis, Theory, Methods and Applications,
Springer.  Sources for individual data files are more fully
documented in the book.

Collection of datasets from Sen & Srivastava: Regression
Analysis, Theory, Methods and Applications, Springer.  Sources
for individual data files are more fully documented in the
book.

Collection of datasets from Sen & Srivastava: "Regression
Analysis, Theory, Methods and Applications", Springer.  Sources
for individual data files are more fully documented in the
book.

Parsimonious Mixtures of MSEN and MTIN Distributions
c(
person(given = "Salvatore D.",
family = "Tomarchio",
role = c("aut","cre"),
email = "daniele.tomarchio@unict.it"),
person(given = "Bagnato",
family = "Luca",
role = "aut"),
person(given = "Antonio",
family = "Punzo",
role = "aut"))
Implements parsimonious mixtures of MSEN and MTIN distributions via expectation-
maximization based algorithms for model-based clustering. For each mixture
component, parsimony is reached via the eigen-decomposition of the scale
matrices and by imposing a constraint on the tailedness parameter. This produces
a family of 28 parsimonious mixture models for each distribution.
doSNOW, foreach, snow, TSdist, tidyr, data.table, expint,
zipfR, mclust, rlist, withr
Salvatore D. Tomarchio [aut, cre],
Bagnato Luca [aut],
Antonio Punzo [aut]