The package contains several factor analysis methods, with emphasis to count data. The main methods rely on copulas to estimate the residual covariance, not explained by the latent structure. Additionally, the package contains four data sets for empirical evaluation and one baseline model. The methods use RStan for inference, the only exception is the iPoiss method where we use conjugate posterior.

Use devtools to install the package directly from the repository. The package needs to compile the models in C++ so a compiler is required.

library(devtools)
devtools::install_github("gregorp90/RStan-package-test")

You may have trouble installing the 32-bit and 64-bit versions on Windows. In that case only install one version with

devtools::install_github("gregorp90/RStan-package-test", INSTALL_opts = c("--no-multiarch"))

which will automatically install the arch that is used in your current RStudio session. If it still does not install, you may need to check your system Path, where the compiler for the desired version should be before the compiler for the other version.

The main functionality of the package is training and predicting with different factor analysis methods. For that, you only need to use functions starting with train_ and pred_. Additionally there are four sports count data sets that can be used for the training and evaluation. The data are from the English association football Premier League (EPL), National Basketball Association (NBA), National Football League (NFL), and esports League of Legends Champions Korea (LCK).

For example, to train, predict, and evaluate the copula negative binomial factor analysis method on EPL data, use

data(EPL_data)
train_ind <- EPL_data$splits[ ,1] == 2
test_ind  <- EPL_data$splits[ ,1] == 1
train_dat <- EPL_data$fa_data[train_ind, ]
train_grp <- EPL_data$group[train_ind]
test_dat  <- EPL_data$fa_data[test_ind, ]
test_grp  <- EPL_data$group[test_ind]

my_model <- train_CNBFA2(train_dat, train_grp)
my_eval  <- pred_CNBFA2(my_model, test_grp, test_dat)
mean(my_eval$likelihoods)
my_eval$predictions # get predictions for all observations in the test set

Inputs to other methods are similar, for details see documentation.