Empirical Likelihood (EL) for Comparing Two Survival Functions
Functions for computing critical values and implementing the one-sided/two-sided EL tests.
Hsin-wen Chang [aut, cre] <hwchang@stat.sinica.edu.tw>

The Empirical Likelihood-Based Consistent Information Criterion
We developed a consistent and robust information criterion to conduct model selection for semiparametric models. It is free of distribution specification and powerful to locate the true model given large sample size. This package provides several usage of ELCIC with applications in generalized linear model (GLM), generalized estimating equation (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanism of missing at random and drop-out. Chixaing Chen, Ming Wang, Rongling Wu, Runze Li (2020) <doi:10.5705/ss.202020.0254>.
knitr, rmarkdown, markdown, testthat (>= 3.0.0)
Chixiang Chen [cre],
Biyi Shen [aut],
Ming Wang [aut]

ELISA Data Analysis with Batch Correction
person("Feng", "Feng", email = "ffeng@BU.edu", role = c("aut", "cre"))
To run data analysis for enzyme-link immunosorbent assays (ELISAs).
Either the five- or four-parameter logistic model will be fitted for data of single ELISA.
Moreover, the batch effect correction/normalization will be carried out, when there are more than one batches of ELISAs.
Feng (2018) <doi:10.1101/483800>.
'BatchCorrection.R' 'ELISAplate.R' 'ELISAtools_IO.R'
'ELISAtools.R' 'Regression.R'

person("Alessio", "Petrozziello", email = "alessio.petrozziello@port.ac.uk", role = c("aut", "cre"))
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.

Implementation of the Efficient Large-Scale Online Display
Advertising Algorithm
c(person("Courtney", "Paulson", email = "cpaulson@rhsmith.umd.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://www-bcf.usc.edu/~gareth/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.
Courtney Paulson [aut, cre],
Lan Luo [ctb],
Gareth James [ctb]