SAGMM

The SAGMM package allows for computation of gaussian mixture models using stochastic approximation to increase efficiency with large data sets. The primary function allows this to be performed in a relative flexible manner.

Installation

You can install SAGMM from github with:

# install.packages("devtools")
# devtools::install_github("andrewthomasjones/SAGMM")
install.packages("SAGMM", repos = c('https://andrewthomasjones.r-universe.dev', 'https://cloud.r-project.org'))

Example

This is a basic example which shows you how to solve a common problem:

library(SAGMM)
#generate sample data
sims<-generateSimData(ngroups=10, Dimensions=10, Number=10^5)

#time fit
ptm <- proc.time()
res1<-SAGMMFit(sims$X, ngroups=10)
#> Warning: Quick-TRANSfer stage steps exceeded maximum (= 5000000)
#> Warning: did not converge in 10 iterations
proc.time()-ptm #should be under 15 seconds for 10 x 10^5 dataset on an ordinary laptop
#>    user  system elapsed 
#>   5.065   0.065   5.211

library(SAGMM)
#generate sample data
sims<-generateSimData(ngroups=5, Dimensions=2, Number=10^3)
#plot
res2<-SAGMMFit(sims$X, sims$Y, plot=T)

Authorship statement

The SAGMM package is co-authored by Andrew T. Jones and Hien D. Nguyen.

Bug reporting and contributions

Thank you for your interest in SAGMM. If you happen to find any bugs in the program, then please report them on the Issues page (https://github.com/andrewthomasjones/SAGMM/issues). Support can also be sought on this page. Furthermore, if you would like to make a contribution to the software, then please forward a pull request to the owner of the repository.