c(person("Courtney", "Paulson", email = "courtneypaulson@suu.edu", role = c("aut", "cre")),person("Lan", "Luo", role="ctb"),person("Gareth","James",role="ctb"))
An implementation of the algorithm described in "Efficient Large-
Scale Internet Media Selection Optimization for Online Display Advertising"
by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below
for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf>
for a full-text version of the paper). The algorithm here is designed to
allocate budget across a set of online advertising opportunities using a
coordinate-descent approach, but it can be used in any resource-allocation
problem with a matrix of visitation (in the case of the paper, website page-
views) and channels (in the paper, websites). The package contains allocation
functions both in the presence of bidding, when allocation is dependent on
channel-specific cost curves, and when advertising costs are fixed at each channel.

Extreme Learning Machine for Survival Analysis
We use the Buckley-James method to impute the data and extend the emerging Extreme Learning Machine approach to survival analysis. Currently, only right censored data are supported. For a detailed information, see the paper by Hong Wang, Jianxin Wang and Lifeng Zhou (2017) <https://github.com/whcsu/ELMSurv/blob/master/elmsurv-revised.pdf>, which will appear in Applied Intelligence <https://link.springer.com/journal/10489> soon.

First, we use the Buckley-James method to impute the data and extend the emerging extreme learning machine approach to survival analysis. Second, we present a kernel extreme learning machine Cox model regularized by an L_0-based broken adaptive ridge (BAR) penalization method. For a detailed information, see Hong Wang et al(2018) <DOI: 10.1007/s10489-017-1063-4>, Hong Wang and Gang Li(2019) <DOI:10.1002/sim.8090>.

ELT - A package to build Experience Life Tables
Julien Tomas, Frederic Planchet, Wassim Youssef
The package is meant to help actuaries to build experience life tables. It contains functions to calculate exposure and number of deaths, smoothing following four different methods, and validation following three criteria.
R (>= 2.10.0), locfit, lattice, latticeExtra, grid, xlsx