calc_exp_TK(rprobs, A, itemwt, p, ip, nitems, resp.ind)
calc_posterior.v2(rprobs, gwt, resp, nitems, resp.ind.list, normalization = TRUE, thetasamp.density = NULL, snodes = 0, resp.ind=NULL, avoid.zerosum=FALSE , logprobs = FALSE)
calc_prob.v5(iIndex, A, AXsi, B, xsi, theta, nnodes, maxK, recalc = TRUE)
stud_prior.v2(theta, Y, beta, variance, nstud, nnodes, ndim, YSD, unidim_simplify, snodes )
tam.jml.xsi(resp, resp.ind, A, B, nstud, nitems, maxK, convM, ItemScore, theta, xsi, Msteps, pweightsM, est.xsi.index)
tam.jml.xsi2(resp, resp.ind, A, A.0, B, nstud, nitems, maxK, convM, ItemScore, theta, xsi, Msteps, pweightsM, est.xsi.index, rp3, rp3.sel, rp3.pweightsM)
mstep.regression(resp, hwt, resp.ind, pweights, pweightsM, Y, theta, theta2, YYinv, ndim, nstud, beta.fixed, variance, Variance.fixed, group, G, snodes = 0 , thetasamp.density = NULL , nomiss=FALSE)
Mstep_slope.v2(B_orig, B, B_obs, B.fixed, max.increment, nitems, A, AXsi, xsi, theta, nnodes, maxK, itemwt, Msteps, ndim, convM, irtmodel, progress, est.slopegroups, E, basispar,se.B, equal.categ)
resp.pattern3(x)
theta.sq(theta) # R version
theta.sq2(theta) # Rcpp version
rowcumsums(m1)
rowMaxs(mat, na.rm = FALSE)
add.lead(x, width=max(nchar(x)))
# calculation of multivariate normal density with matrix input of mean
dmvnorm_TAM( x , mean , sigma , log = FALSE ) # see mvtnorm::dmvnorm