readme <- function(){
cat('
### Environments:
# - .GlobalEnv is not allowed to modify according to CRAN policy.
# - .pmclustEnv: a substitute environment for .GlobalEnv and storage.
### Prespecified variables:
# (identical)
# CONTROL: list[3]
# - max.iter: integer[1], number of maximum iterations.
# - abs.err: double[1], absolute tolerance.
# - rel.err: double[1], relative tolerance.
# - debug: integer[1], level of debug.
# - RndEM.iter: integer[1], number of iterations of RndEM.
# - exp.min: double[1], minimum exponent for base e.
# - exp.max: double[1], maximum exponent for base e.
# COMM.SIZE: integer[1], total spmds.
# p.times.logtwopi: p * log(2 * pi), for log likelihood.
# (different)
# COMM.RANK: integer[1], rank in the communicator.
# X.spmd: double[N.spmd, p], data.
# ID.sample.spmd: integer[], sampling id point to the large dataset.
### Global variables:
# (different)
# Z.spmd: double[N.spmd, K], posterior probability.
# Z.colSums: double[K], sum of posterior probability.
# W.spmd: double[N.spmd, K], conditional log posterior probability.
# W.spmd.rowSums: double[N.spmd], log density for each observations.
# U.spmd: double[N.spmd, K], W.spmd plus log eta.
# CLASS.spmd: double[N.spmd], classification of observations.
# CHECK: list[4], for output.
# - algorithm: string[1], "em", "aecm", "apecm", "apecma", or "kmeans".
# - i.iter: integer[1], current iteration.
# - abs.err: double[1], current absolute tolerance.
# - rel.err: double[1], current relative tolerance.
# - convergence: integer[1], status of convergence.
# * 0: keep running.
# * 1: converge successfully.
# * 2: run out of iterations.
# * 3: logL decreasing.
### Local variables: subjected to updated within EM iterations.
# (identical/bcast)
# PARAM: list, contains all prameters changed over iterations.
# - N: integer[1], total number of observations.
# - p: integer[1], variable dimension. (p > 1)
# - K: integer[1], number of clusters.
# - ETA: double[K], mixing proportion.
# - log.ETA: double[K], log of mixing proportion.
# - MU: double[p, K], centers.
# - SIGMA: list[K], dispersions.
# - SIGMA[[k]]: double[p, p], for each component.
# * convert to double[p * (p + 1) / 2] for LAPACK.
# - logL: double[1], current log likelihood.
# - min.N.CLASS: integer[1], minimum size of cluster.
### Output:
# (different)
# CLASS.spmd
# (rank = 0, only, identical)
# PARAM, CHECK
')
} # End of readme().
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.