“…To analyze these networks, we developed a stochastic blockmodel that accounts for censoring introduced by the focal-follow methodology. More specifically, we extend an existing model for binary cross-sectional data [see ( 54 , 55 )] to adjust for the uneven sampling that characterized our repeated-observation design. Our model can be written succinctly asY[i,j,d]∼Bernoullifalse(ϕfalse[i,jfalse]false)∣i<j∧C[i,j,d]=1∧Z[i,j,d]=1logitfalse(ϕfalse[i,jfalse]false)=normalα[Afalse(ifalse),Afalse(jfalse)]+X[i,j]βwhere Y [ i , j , d ] is an indicator for if individuals i and j were observed coforaging together on day d , ϕ [ i , j ] is the predicted probability that individuals i and j coforage together, C [ i , j , d ] is an indicator of whether individuals i and j were both in-camp on day d , Z [ i , j , d ] is a censoring mask for day d (defined in more detail later), α is a K × K matrix of within- and between-age-class intercept offset parameters, A(i) is a function returning the age class of individual i , β is a vector of regression coefficients, and X [ i , j ] is a row vector of dyadic covariate data.…”