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Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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inst/CITATION

inst/COPYRIGHTS

inst/code/altsqp_original.R inst/code/check_map.R inst/code/check_poisson_hessian.R inst/code/compile_newsgroups_results_for_annotation.R inst/code/compute_newsgroups_topics.R inst/code/droplet.R inst/code/lda.R inst/code/multinom_demo.R inst/code/pbmc_de_analysis.Rmd inst/code/pbmc_demo.R inst/code/plsi.R inst/code/pois_vs_binom.R inst/code/pois_vs_multinom.R inst/code/poisson_demo.R inst/code/postfit_motif_analysis_Buenrostro2018.R inst/code/pseudocounts.R inst/code/scd.R inst/code/simulate_data_for_sfa.R inst/code/test_hpd.R inst/code/test_poisson_fit.R inst/code/test_poisson_fit_basic.R

inst/datafiles/newsgroups.RData

inst/datafiles/newsgroups_topics.RData

man/annotation_heatmap.Rd man/compare_fits.Rd man/de_analysis.Rd man/embedding_plots.Rd man/embeddings_from_topics.Rd man/fit_multinom_model.Rd man/fit_poisson_nmf.Rd man/fit_topic_model.Rd man/likelihood.Rd man/loadings_plot.Rd man/merge_topics.Rd man/multinom2poisson.Rd man/newsgroups.Rd man/pbmc_facs.Rd man/plot_loglik_vs_rank.Rd man/plot_progress.Rd man/poisson2multinom.Rd man/predict.Rd man/run_homer.Rd man/select_loadings.Rd man/simulate_count_data.Rd man/simulate_gene_data.Rd man/simulate_toy_gene_data.Rd man/structure_plot.Rd man/summary.poisson_nmf_fit.Rd man/volcano_plot.Rd

src/Makevars

src/Makevars.win

src/RcppExports.cpp

src/ccd.cpp

src/cost.cpp

src/cost.h

src/misc.cpp

src/misc.h

src/mixem.cpp

src/mixem.h

src/pnmfem.cpp

src/poismix.cpp

src/poismix.h

src/poisson.cpp

src/scd.cpp

stephenslab/fastTopics documentation built on March 29, 2025, 3:24 p.m.

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stephenslab/fastTopics
Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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stephenslab/fastTopics Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data

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Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data