Fast Topic Models Using Varimax

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Documentation for package ‘tmfast’ version 0.1.1

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

tmfast-package	Fitting "topic models" with PCA+varimax
build_matrix	Convert a long dataframe to a wide (sparse) matrix
compare_betas	Compare topic-word distributions using Hellinger distance
draw_corpus	Draw a collection of documents
entropy	Entropy of a distribution
expected_entropy	Expected entropy for samples from a Dirichlet distribution
fit_varimax	Given a (rank 'n') PCA fit, return a rank 'k < n' varimax fit
hellinger	Hellinger distances
hellinger.data.frame	Hellinger distances
hellinger.Matrix	Hellinger distances
hellinger.matrix	Hellinger distances
insert_topics	Insert a topic model into a fitted 'tmfast'
journal_specific	"Journal-specific" simulation scenario
loadings	Extract a PCA/varimax loadings matrix
loadings.default	Extract a PCA/varimax loadings matrix
ndH	Information gain (uniform distribution)
ndR	Information gain (length-proportional distribution)
peak_alpha	Alpha parameter with a single peak
predict.varimaxes	Project new data into PCA score space
rdirichlet	Sample from the Dirichlet distribution
renorm	Renormalize tidied distributions
rotation	Extract varimax rotation
scores	Extract item scores from a fitted PCA/varimax model
solve_power	Solve the equation to find the desired exponent
target_power	Find target power for renormalization
tidy.tmfast	Extract beta and gamma matrices from 'tmfast' objects
tidy_all	Extract gamma or beta matrices for all topics
tmfast	Fit a topic model using PCA+varimax
tsne	Discursive space using t-SNE
tsne.data.frame	Discursive space using t-SNE
tsne.STM	Discursive space using t-SNE
tsne.tmfast	Discursive space using t-SNE
umap	Discursive space using UMAP
umap.matrix	Discursive space using UMAP
umap.STM	Discursive space using UMAP
umap.tmfast	Discursive space using UMAP
varimax_irlba	Fit a varimax-rotated PCA using irlba