According to a phenomenon known as "the wisdom of the crowds," 
    combining point estimates from multiple judges often provides a 
    more accurate aggregate estimate than using a point estimate from
    a single judge. However, if the judges use shared information in 
    their estimates, the simple average will over-emphasize this common
    component at the expense of the judges’ private information. 
    Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds
    Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions"
    <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes
    a procedure for calculating a weighted average of the judges’ individual
    estimates such that resulting aggregate estimate appropriately combines
    the judges' collective information within a single estimation problem. 
    The authors use both simulation and data from six experimental studies
    to illustrate that the weighting procedure outperforms existing averaging-like
    methods, such as the equally weighted average, trimmed average, and median.
    This aggregate estimate -- know as "the knowledge-weighted estimate" --
    inputs a) judges' estimates of a continuous outcome (E) and 
    b) predictions of others' average estimate of this outcome (P).  
    In this R-package, the function knowledge_weighted_estimate(E,P) 
    implements the knowledge-weighted estimate. Its use is illustrated
    with a simple stylized example and on real-world experimental data.

An implementation of metaheuristic algorithms for continuous optimization. Currently, the package contains the implementations of 21 algorithms, as follows: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>),  whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>), clonal selection algorithm (Castro, 2002 <doi:10.1109/TEVC.2002.1011539>), differential evolution (Das & Suganthan, 2011), shuffled frog leaping (Eusuff, Landsey & Pasha, 2006), cat swarm optimization (Chu et al., 2006), artificial bee colony algorithm (Karaboga & Akay, 2009), krill-herd algorithm (Gandomi & Alavi, 2012), cuckoo search (Yang & Deb, 2009), bat algorithm (Yang, 2012), gravitational based search (Rashedi et al., 2009) and black hole optimization (Hatamlou, 2013).