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Function to fit the bipartite latent space model (BLSM) outlined in Wang et al. (2021)
blsm(Niter, Y.ia, D)
number of iterations
N by M matrix containing the binary multivariate attributes
number of dimensions in the data
list containing:
lsmhEZ.i (N x D) matrix containing the posterior means of the latent person positions
lsmhEZ.i
N
D
lsmhEZ.a (M x D) matrix containing the posterior means of the latent item positions
lsmhEZ.a
M
lsmhVZ.0 (D x D) matrix containing the posterior variance of the latent person positions
lsmhVZ.0
lsmhVZ.1 (D x D) matrix containing the posterior variance of the latent item positions
lsmhVZ.1
lsmhAlpha.1 scaler of mean of the posterior distributions of \(\alpha.1\)
lsmhAlpha.1
lsmhKL expected log-likelihood
lsmhKL
# NOT RUN { attach(french) a=blsm(Niter=10,Y.ia,D=2) # }
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