Imports: Rcpp (>= 1.0.0), ggplot2, grid, utils, glue, data.table, stats

Title: An Interface to the 'fastText' Library
Description: An interface to the 'fastText' library
can be used for text classification and to learn word vectors.
The install folder contains the 'PATENTS' file.
An example how to use 'fastTextR' can be found in the 'README' file.
Imports: stats, graphics, Rcpp (>= 0.12.4)

c(person("Florian", "Schwendinger", role = "aut"),
Imports: stats, graphics, Rcpp (>= 0.12.4), slam
Packaged: 2020-09-10 06:35:05 UTC; emilhvitfeldthansen
Emil Hvitfeldt [aut, cre] (<https://orcid.org/0000-0002-0679-1945>)

Packaged: 2022-10-31 00:12:04 UTC; emilhvitfeldthansen

Title: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix
Description: Implements fast, scalable optimization algorithms for
fitting topic models ("grade of membership" models) and
non-negative matrix factorizations to count data. The methods
exploit the special relationship between the multinomial topic
model (also, "probabilistic latent semantic indexing") and Poisson
non-negative matrix factorization. The package provides tools to
compare, annotate and visualize model fits, including functions to
efficiently create "structure plots" and identify key features in
topics. The 'fastTopics' package is a successor to the 'CountClust'
Imports: graphics, utils, methods, stats, Matrix, MCMCpack, quadprog,
irlba, dplyr, Rtsne, uwot, ashr, Rcpp (>= 1.0.1), RcppParallel
(>= 4.4.1), parallel, progress, pbapply, ggplot2 (>= 3.3.0),
ggrepel (>= 0.9.0), cowplot, plotly, htmlwidgets
Suggests: NNLM, Ternary, testthat, knitr, rmarkdown

Description: This package is designed for time series data.  Uses fast
implementations to estimate Vector Autoregressive models and
Vector Autoregressive models with Exogenous Inputs.  For
speedup, fastVAR can use multiple cpu cores to calculate the
estimates.  For very large systems, fastVAR uses Lasso penalty
to return very sparse coefficient matrices. Regression
diagnostics can be used to compare models, and prediction
functions can be used to calculate the n-step ahead prediction.
Map-Reduce functions are in the works (Beta) for estimating