API for matei-ionita/Tailor
Gaussian mixture modeling for heavy-tailed distributions in Flow Cytometry data

Global functions
as_matrix Source code
binary_search Source code
bulk_weighted_gmm Source code
categorical_labeling Man page Source code
categorical_merging Source code
cluster_phenotype_label Source code
compute_bin_variance Source code
compute_labels Source code
correct_variances Source code
customize_1D_mixtures Man page Source code
e_step Source code
find_bin_mean Source code
find_to_merge Source code
get_1D_cutoffs Source code
get_1D_mixtures Man page Source code
get_1D_mixtures_custom Source code
get_1D_mixtures_default Source code
get_bin_list Source code
get_bin_summary Source code
get_cluster_means Source code
get_cluster_sds Source code
get_init Source code
get_legend Source code
get_single_cutoff Source code
get_tsne_centers Source code
get_tsne_clusters Source code
get_unique_phenotypes Source code
get_weighted_subsample Source code
initialize_mixture Source code
inspect_1D_mixtures Man page Source code
learn_1D_mixtures_parallel Source code
learn_1D_mixtures_sequential Source code
m_step Source code
make_cluster_phenobars Man page Source code
make_kdes_global Source code
map_events_to_bins Source code
plot_cluster_histograms Source code
plot_distribution_1d Source code
plot_kde_vs_mixture Source code
plot_kdes_global Man page
plot_tailor_fluorescence Man page Source code
plot_tailor_majpheno Man page Source code
print_kdes_with_cutoffs Source code
split_bin_kmeans Source code
start_parallel_cluster Source code
tailor_learn Man page Source code
tailor_map_parallel Source code
tailor_map_sequential Source code
tailor_predict Man page Source code
matei-ionita/Tailor documentation built on Jan. 4, 2021, 11:47 a.m.