Model Fitting to Progressively Censored Mixture Data
c(person("Lida", "Fallah", role = c("aut", "cre"), email =
"l.fallah22@gmail.com"), person("John", "Hinde", role = "aut"))
Functions for generating progressively Type-II censored data in
a mixture structure and fitting models using a constrained EM algorithm. It can also
create a progressive Type-II censored version of a given real dataset to be considered for
model fitting.
Lida Fallah [aut, cre],
John Hinde [aut]

Provide methods to iterate through all
permutations/combinations without retrieving all (avoid RAM
overflow)

Principal Component of Explained Variance
c(
person("Maxime", "Turgeon", email = "maxime.turgeon@mail.mcgill.ca",
role = c("aut", "cre")),
person("Aurelie", "Labbe",
role = c("aut")),
person("Karim", "Oualkacha",
role = c("aut")))
Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate
response vector. It is a dimension-reduction technique, similar to Principal
component analysis (PCA), which seeks the maximize the proportion of
variance (in the response vector) being explained by a set of covariates.
Maxime Turgeon [aut, cre],
Aurelie Labbe [aut],
Karim Oualkacha [aut]

c(
person("Maxime", "Turgeon", email = "maxime.turgeon@mail.mcgill.ca",
role = c("aut", "cre")),
person("Aurelie", "Labbe",
role = c("aut")),
person("Karim", "Oualkacha",
role = c("aut")),
person("Stepan", "Grinek",
role = c("aut")))
Principal component of explained variance (PCEV) is a statistical
tool for the analysis of a multivariate response vector. It is a dimension-
reduction technique, similar to Principal component analysis (PCA), that seeks
to maximize the proportion of variance (in the response vector) being explained
by a set of covariates.
Maxime Turgeon [aut, cre],
Aurelie Labbe [aut],
Karim Oualkacha [aut],
Stepan Grinek [aut]

Preconditioned Conjugate Algorithm for solving Ax=b
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.