socialInductionWeighted: Social induction model with weights
In tbonne/SocialIntegrationNet:

This function runs the social induction model of network formation (see Ilany et al. 2016), with the addition of weighted edges.

1 2	socialInductionWeighted(obsNet = startingNet, Pn, Pr, Pb, En1, En2, Er1, Er2, maxE, iter)

`obsNet`	Starting network. This must be a weighted and non-directed igraph network.
`Pn`	Probability of forming ties with a mother's partner.
`Pr`	Probability of forming ties with a non mother's partner.
`Pb`	Probability of forming ties with mother.
`En1`	Alpha parameter for effort into a mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.
`En2`	Beta parameter for effort into a mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.
`Er1`	Alpha parameter for effort into a non mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.
`Er2`	Beta parameter for effort into a non mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.
`maxE`	Maximum grooming effort used to scale the beta distribution. This choice should represent an upper limit of effort.
`iter`	Number of iteration, where each iteration removes one node and introduces a new one.

startingNet = erdos.renyi.game(n=10, type = c("gnm"), p.or.m = 15)
E(startingNet)$weight<-c(2,25,1,1,1,1,4,5,6,9,2,1,2,1,2)
predWeightedNet<-socialInductionWeighted(startingNet, Pn=0.4,Pr=0.1,Pb=1,En1=0.1,En2=4,Er1=0.1,Er2=4,maxE=100,iter=100)
plot(startingNet, edge.width=E(startingNet)$weight)
plot(predWeightedNet, edge.width=E(predWeightedNet)$weight)