EM Algorithm for Mixtures-of-Experts

Internal intialization functions for EM algorithms in the package `mixtools`

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
flaremix.init(y, x, lambda = NULL, beta = NULL, sigma = NULL,
alpha = NULL)
gammamix.init(x, lambda = NULL, alpha = NULL, beta = NULL,
k = 2)
logisregmix.init(y, x, N, lambda = NULL, beta = NULL, k = 2)
multmix.init(y, lambda = NULL, theta = NULL, k = 2)
mvnormalmix.init(x, lambda = NULL, mu = NULL, sigma = NULL,
k = 2, arbmean = TRUE, arbvar = TRUE)
normalmix.init(x, lambda = NULL, mu = NULL, s = NULL, k = 2,
arbmean = TRUE, arbvar = TRUE)
poisregmix.init(y, x, lambda = NULL, beta = NULL, k = 2)
regmix.init(y, x, lambda = NULL, beta = NULL, s = NULL, k = 2,
addintercept = TRUE, arbmean = TRUE, arbvar=TRUE)
regmix.lambda.init(y, x, lambda = NULL, beta = NULL, s = NULL,
k = 2, addintercept = TRUE, arbmean = TRUE,
arbvar = TRUE)
regmix.mixed.init(y, x, w = NULL, sigma = NULL,
arb.sigma = TRUE, alpha = NULL, lambda = NULL,
mu = NULL, R = NULL, arb.R = TRUE, k = 2,
mixed = FALSE, addintercept.fixed = FALSE,
addintercept.random = TRUE)
repnormmix.init(x, lambda = NULL, mu = NULL, s = NULL, k = 2,
arbmean = TRUE, arbvar = TRUE)
segregmix.init(y, x, lambda = NULL, beta = NULL, s = NULL, k = 2,
seg.Z, psi, psi.locs = NULL)
``` |

These are usually not to be called by the user. Definitions of the arguments appear in the respective EM algorithms.

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