c(person("Chul", "Kim", role = c("aut", "cre"),
email = "statkim7578@yonsei.ac.kr"),
person("Sanghoon", "Cho", role = c("aut"),
email = "Sanghoon.Cho@grad.moore.sc.edu "),
person("Jongho", "Im", role = c("aut"),
email = "ijh38@yonsei.ac.kr"))
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.
tibble, magrittr, rlang, dplyr, numDeriv, Rcpp
Chul Kim [aut, cre],
Sanghoon Cho [aut],
Jongho Im [aut]

Connect R with MOA for Massive Online Analysis
c(person("Jan", "Wijffels", role = c("aut", "cre"), email =
"jwijffels@bnosac.be"), person("BNOSAC", role = "cph"))
Connect R with MOA (Massive Online Analysis -
<http://moa.cms.waikato.ac.nz>) to build classification models and
regression models on streaming data or out-of-RAM data.
Also streaming recommendation models are made available.
RMOAjars (>= 1.0), rJava (>= 0.6-3), methods
Code is Copyright (C) Jan Wijffels and BNOSAC

Connect R with MOA (Massive Online Analysis -
<https://moa.cms.waikato.ac.nz>) to build classification models and
regression models on streaming data or out-of-RAM data.
Also streaming recommendation models are made available.

Connect R with MOA (Massive Online Analysis -
http://moa.cms.waikato.ac.nz) to build classification models and
regression models on streaming data or out-of-RAM data

Connect R with MOA (Massive Online Analysis -
<https://moa.cms.waikato.ac.nz/>) to build classification models and
regression models on streaming data or out-of-RAM data.
Also streaming recommendation models are made available.

External jars required for package RMOA. RMOA is a framework to
build data stream models on top of MOA (Massive Online Analysis -
<http://moa.cms.waikato.ac.nz>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.