Package index
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bk2018_params - Default population parameters for the Bakk & Kuha (2018) simulation
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coef(<tseLCA_measurement>)coef(<tseLCA_covariate>)coef(<tseLCA_distal>)coef(<tseLCA_both>) - Extract coefficients from a tseLCA model object
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draw_Zo() - Draw a continuous distal outcome given true class memberships (scenario "distal")
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draw_Zp() - Draw the covariate Zp ~ Uniform{1, 2, 3, 4, 5}
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draw_classes() - Draw latent class memberships from their marginal distribution
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draw_classes_given_Zp() - Draw latent classes conditional on the covariate (scenario "covariate")
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draw_indicators() - Draw binary indicators given true class memberships
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fitZ_from_fit0() - Estimate covariate effects with measurement parameters fixed (two-step EM)
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fitZ_from_multiLCA() - Estimate two-step covariate model via multilevLCA (optional reference path)
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generate_all_conditions() - Generate datasets for all 18 conditions in the simulation design
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generate_data() - Generate one dataset following the Bakk & Kuha (2018) simulation design
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lca_step1() - Fit the LCA measurement model (Step 1)
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make_rho() - Build the item-response probability matrix for the simulation
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mnl_probs() - Compute multinomial logistic class probabilities given covariates
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plot(<tseLCA_measurement>)plot(<tseLCA_covariate>)plot(<tseLCA_distal>)plot(<tseLCA_both>) - Plot item-response probability profiles for a tseLCA model
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print(<tseLCA_measurement>)print(<tseLCA_covariate>)print(<tseLCA_distal>)print(<tseLCA_both>) - Print a tseLCA model object
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summary(<tseLCA_measurement>)summary(<tseLCA_covariate>)summary(<tseLCA_distal>)summary(<tseLCA_both>) - Summarize a tseLCA model object
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three_step() - Three-step LCA estimation with covariates and/or distal outcomes
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vcov(<tseLCA_measurement>)vcov(<tseLCA_covariate>)vcov(<tseLCA_distal>)vcov(<tseLCA_both>) - Extract the variance-covariance matrix from a tseLCA model object