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.

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.

Model-based clustering for paired data based on the regression of a mixture of Bayesian hierarchical models on covariates. Zhang et al. (2023) <doi:10.1186/s12859-023-05556-x>.

A flexible framework for calculating Elo ratings and resulting
    rankings of any two-team-per-matchup system (chess, sports leagues, 'Go',
    etc.). This implementation is capable of evaluating a variety of matchups,
    Elo rating updates, and win probabilities, all based on the basic Elo
    rating system. It also includes methods to benchmark performance,
    including logistic regression and Markov chain models.

Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.