Title: Implementations of Semi-Supervised Learning Approaches for
Authors@R: c(person("Jesse", "Krijthe", role = c("aut", "cre"),
Imports: methods, Rcpp, MASS, kernlab, quadprog, Matrix, dplyr, tidyr,
Suggests: testthat, rmarkdown, SparseM, numDeriv, LiblineaR
Description: A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.
'RcppExports.R' 'S4VM.R' 'SVM.R' 'SelfLearning.R' 'TSVM.R'

Description: A collection of implementations of semi-supervised classifiers
and methods to evaluate their performance. The package includes implementations
of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
Likelihood estimation, S4VM and WellSVM.

Title: Simulation of Supply Reservoir Systems using Standard Operation
Author: Rezgar Arabzadeh, Parisa Aberi, Kaveh Panaghi, Shahab Araghinejad, Majid Montaseri
Description: Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.

Author: Busra Sevinc, Bekir Cetintav, Melek Esemen, Selma Gurler
Description: Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
Packaged: 2018-03-19 09:52:28 UTC; kullanici