Packaged: 2019-01-17 15:20:21 UTC; Pedro Albuquerque

Title: Functionality to extract weather information from METAR reports.
Authors@R: c(person("Jan", "Wijffels", role = c("aut", "cre", "cph"),
email = "jwijffels@bnosac.be"), person("Eric", "McCarthy", role
Copyright: C code in the src folder is taken and adapted from
belongs to Eric McCarthy. This code has LGPL-2.1 license.
Description: Functions to extract weather information from METAR reports
(Aviation Routine Weather Reports). Using the METAR Decoder Software
Package Library (mdsplib) which is provided by the National Weather

Title: Multiple Kernel Learning for Classification or Regression
Description: Provides R and C++ function that enable the user to conduct multiple
kernel learning (MKL) and cross validation for support vector machine (SVM) models.
Cross validation can be used to identify kernel shapes and hyperparameter
combinations that can be used as candidate kernels for MKL. There are three
implementations provided in this package, namely SimpleMKL Alain Rakotomamonjy et. al
(2008), Simple and Efficient MKL Xu et. al (2010), and Dual augmented Lagrangian MKL
Suzuki and Tomioka (2011) <doi:10.1007/s10994-011-5252-9>. These methods identify the convex combination of
candidate kernels to construct an optimal hyperplane.
Imports: Rcpp (>= 1.0.0), caret, kernlab, stats, e1071

Title: Sundry Discrete Probability Distributions
Author: Robert M. Kirkpatrick <rkirkpatrick2@vcu.edu>
Maintainer: Robert M. Kirkpatrick <rkirkpatrick2@vcu.edu>
Description: Sundry discrete probability distributions and helper functions.

Title: Maximum Likelihood Principal Component Analysis
Description: R implementation of Maximum Likelihood Principal Component Analysis
The main idea of this package is to have an alternative way of PCA for
subspace modeling that considers measurement errors.
More details can be found in Peter D. Wentzell (2009)
Maintainer: Renan Santos Barbosa <renansantosbarbosa@usp.br>

Authors@R: c(person("Chul", "Kim", role = c("aut", "cre"),
person("Sanghoon", "Cho", role = c("aut"),
Description: The RMM  fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.
Imports: tibble, magrittr, rlang, dplyr, numDeriv, Rcpp