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mlstm

mlstm: Multilevel Supervised Topic Models with Multiple Outcomes in R

Overview

mlstm implements Multilevel Supervised Topic Models (MLSTM), a probabilistic framework for analyzing text data with multiple associated outcome variables.

Unlike standard supervised topic models that assume a single response per document, MLSTM allows multiple outcomes and introduces a hierarchical regression structure to share information across them.

The package provides efficient variational inference algorithms implemented in C++ via Rcpp, enabling scalable estimation for large text corpora.

Key Features

  • Multi-output supervised topic modeling
  • Hierarchical regression structure across outcomes
  • Variational Bayesian inference (fast and scalable)
  • Supports missing outcome values
  • C++ backend via RcppParallel for performance

Installation 

# install.packages("remotes")
remotes::install_github("thimeno1993/mlstm")

Quick Example

Simulated corpus 

library(mlstm)
set.seed(123)

D <- 50
V <- 200
K <- 5

NZ_per_doc <- 20
NZ <- D * NZ_per_doc

count <- cbind(
  d = rep(0:(D - 1), each = NZ_per_doc),
  v = sample.int(V, NZ, replace = TRUE) - 1L,
  c = rpois(NZ, 3) + 1
)

Y <- cbind(
  y1 = rnorm(D),
  y2 = rnorm(D)
)

LDA 

mod_lda <- run_lda_gibbs(
  count = count,
  K     = K,
  alpha = 0.1,
  beta  = 0.01,
  n_iter = 20,
  verbose = FALSE
)

str(mod_lda$theta)
str(mod_lda$phi)

Supervised Topic Model (STM) 

y <- Y[, 1]

set_threads(2)

mod_stm <- run_stm_vi(
  count = count,
  y     = y,
  K     = K,
  alpha = 0.1,
  beta  = 0.01,
  max_iter = 50,
  min_iter = 10,
  verbose  = FALSE
)

y_hat <- ((mod_stm$nd / mod_stm$ndsum) %*% mod_stm$eta)[, 1]
cor(y, y_hat)

Multi-output STM (MLSTM) 

J <- ncol(Y)

mu      <- rep(0, K)
upsilon <- K + 2
Omega   <- diag(K)

mod_mlstm <- run_mlstm_vi(
  count  = count,
  Y      = Y,
  K      = K,
  alpha  = 0.1,
  beta   = 0.01,
  mu     = mu,
  upsilon = upsilon,
  Omega   = Omega,
  max_iter = 50,
  min_iter = 10,
  verbose  = FALSE
)

Y_hat <- ((mod_mlstm$nd / mod_mlstm$ndsum) %*% mod_mlstm$eta)
cor(Y, Y_hat)

Data Format

Each row of count represents one non-zero document-term entry.

column description
d document index (0-based)
v word index (0-based)
c token count

Performance

  • Implemented in C++ via Rcpp
  • Parallelized with RcppParallel
  • Suitable for large-scale text and supervised learning

Documentation

References

  • Himeno T, Yokouchi D (2023). “A Multi-Label Supervised Topic Model for Financial Market Analysis Using News (in Japanese).” JAFEE Journal, 21, 1–28.
  • Himeno, T. and Yokouchi, D. (2026). “mlstm: Multilevel Supervised Topic Models with Multiple Outcomes in R.” (Under submission to Journal of Statistical Software)

Author

Tomoya Himeno

License

MIT License

Development 

devtools::load_all()
devtools::test()
devtools::check()

Issues

https://github.com/thimeno1993/mlstm/issues