Man pages for antedep
Antedependence Models for Longitudinal Data
| aic_cat | Akaike information criterion for fitted categorical AD models |
| aic_gau | Akaike information criterion for fitted Gaussian AD models |
| aic_inad | Akaike information criterion for fitted INAD models |
| Bell | The Bell distribution |
| bic_cat | Bayesian information criterion for fitted categorical AD... |
| bic_gau | Bayesian information criterion for fitted Gaussian AD models |
| bic_inad | Bayesian information criterion for fitted INAD models |
| bic_order_cat | BIC-based order selection for categorical AD models |
| bic_order_gau | BIC-based order selection for Gaussian AD models |
| bic_order_inad | BIC-based order selection for INAD models |
| bolus_inad | Morphine bolus analgesia counts |
| Bz | Bell numbers |
| cattle_growth | Cattle growth data (Treatments A and B) |
| ci_cat | Confidence intervals for fitted categorical AD models |
| ci_gau | Confidence intervals for fitted Gaussian AD models |
| ci_inad | Confidence intervals for fitted INAD models |
| cochlear_implant | Cochlear implant speech recognition data |
| dot-bell_mean_from_theta | Convert Bell parameter to Bell mean |
| dot-bell_theta_from_mean | Convert Bell mean to Bell parameter |
| dot-blend_cat_params_em | Blend old/new CAT parameters and renormalize probabilities |
| dot-build_gau_covariance | Build AD covariance matrix from parameters |
| dot-cat_prob_to_theta | Convert probability row to unconstrained logits |
| dot-cat_theta_to_prob | Convert unconstrained logits to probability row |
| dot-ci_alpha_theta_louis_fe | Compute alpha and theta CIs using Louis' identity (general... |
| dot-ci_wald_i_louis_fe | Wald CI at time i using Louis' identity |
| dot-compute_marginal_ci | Compute CIs for marginal parameters |
| dot-compute_transition_ci | Compute CIs for transition parameters |
| dot-construct_gau_covariance | Construct AD covariance matrix from fitted model |
| dot-count_cells_cat | Count cells for contingency table |
| dot-count_cells_table_cat | Count cells efficiently using table() |
| dot-count_params_cat | Count free parameters for AD(p) categorical model |
| dot-count_params_gau | Count parameters for AD model |
| dot-count_params_homogeneity | Count parameters for homogeneity test |
| dot-counts_to_probs_cat | Convert counts to probabilities (with safe division) |
| dot-counts_to_transition_probs | Compute transition probabilities from counts (simpler... |
| dot-default_marginal_cat | Create default marginal parameters (uniform) |
| dot-default_transition_cat | Create default transition parameters (uniform) |
| dot-em_e_step_gau | E-step: Compute expected sufficient statistics |
| dot-em_m_step_gau | M-step: Update parameters from sufficient statistics |
| dot-expected_counts_to_cell_counts_cat_em | Convert EM expected counts to fit_cat-style cell_counts |
| dot-extract_conditional_probs | Extract conditional probabilities from transition array |
| dot-fit_cat_single_pop | Fit CAT model for a single population |
| dot-fit_cat_single_pop_marginalize | Fit CAT model with missing data via observed-data likelihood... |
| dot-fit_cat_stationary | Fit stationary categorical AD model |
| dot-fit_cat_stationary_single | Fit stationary model for single population |
| dot-fit_cat_timeinvariant | Fit time-invariant categorical AD model |
| dot-fit_cat_timeinvariant_single | Fit time-invariant model for single population |
| dot-fit_gau_em | EM algorithm for AD with missing data |
| dot-fit_inad_heterogeneous | Fit fully heterogeneous INAD model |
| dot-get_combinations_cat | Get all category combinations of given length |
| dot-get_history | Extract history for a subject at time k |
| dot-group_means_gau | Compute group-specific means for two-sample case |
| dot-history_to_index | Convert history to array index |
| dot-inad_build_transitions | Build transition objects for truncated-state missing-data... |
| dot-inad_conv_trunc | Truncated discrete convolution |
| dot-inad_effective_innovation_mean | Effective innovation mean under block effects |
| dot-inad_effective_innovation_param | Effective innovation distribution parameter under block... |
| dot-inad_make_thin_pmf | Build truncated thinning pmf for one previous count |
| dot-inad_order2_dist | Retrieve/calculate order-2 transition distribution for one... |
| dot-inad_state_max | Heuristic state-space bound for INAD missing-data recursion |
| dot-inad_subject_ll_order0 | Subject-level observed-data likelihood for INAD(0) |
| dot-inad_subject_ll_order1 | Subject-level observed-data likelihood for INAD(1) |
| dot-inad_subject_ll_order2 | Subject-level observed-data likelihood for INAD(2) |
| dot-initialize_gau_em | Initialize parameters for AD EM algorithm |
| dot-initialize_gau_marginal | Initialize from marginal statistics (ignoring dependence) |
| dot-innovation_var_gau | Compute innovation variance estimates under AD(p) |
| dot-innov_vec | Innovation probability vector |
| dot-logL_from_counts_cat | Compute log-likelihood from cell counts (faster for large... |
| dot-logL_gau_missing | Compute observed-data log-likelihood for AD with missing... |
| dot-loglik_contribution | Compute log-likelihood contribution from counts and... |
| dot-logL_inad_missing | Observed-data INAD likelihood with missing values under MAR |
| dot-logL_subject_cat | Compute log-likelihood contribution from one subject |
| dot-logL_subject_cat_marginalize_p1 | Observed-data log-likelihood for order-1 CAT model |
| dot-logL_subject_cat_marginalize_p2 | Observed-data log-likelihood for order-2 CAT model |
| dot-logL_subject_cat_observed | Compute observed-data log-likelihood contribution from one... |
| dot-log_sum_exp | Log-sum-exp for numerical stability |
| dot-logsumexp_cat | Stable log-sum-exp |
| dot-louis_info_i_fe | Louis' identity observed information at time i |
| dot-mle_mean_gau | Compute MLE of mean vector under AD(p) |
| dot-pack_cat_params | Pack CAT parameters into unconstrained vector |
| dot-partial_corr_gau | Compute intervenor-adjusted sample partial correlation |
| dot-partial_corr_matrix_gau | Compute matrix of intervenor-adjusted partial correlations |
| dot-poster_general | Posterior computations for all thinning-innovation... |
| dot-prism_residuals | Compute partial residuals for PRISM plot |
| dot-psi_kenward | Modified psi function for test statistic correction |
| dot-rss_gau | Compute residual sum of squares from AD regression |
| dot-rss_two_sample_gau | Compute RSS for two-sample case (pooled and separate) |
| dot-rss_vector_gau | Compute RSS vector for all time points under AD(p) |
| dot-safeguard_update_cat_em | Safeguard M-step update via step-halving |
| dot-safe_log | Safe log function (0 * log(0) = 0) |
| dot-simulate_subject_cat | Simulate one subject's trajectory |
| dot-thin_vec | Thinning probability vector |
| dot-uniform_cat_params | Build uniform CAT parameter values |
| dot-unpack_alpha | Unpack alpha parameters |
| dot-unpack_cat_params | Unpack unconstrained CAT parameter vector |
| dot-validate_blocks_cat | Validate blocks parameter |
| dot-validate_params_cat | Validate transition probability parameters |
| dot-validate_y_cat | Validate categorical data matrix |
| em_cat | EM algorithm for categorical AD model estimation |
| em_gau | EM algorithm for Gaussian AD model estimation |
| em_inad | EM algorithm for INAD model estimation |
| fit_cat | Fit categorical antedependence model by maximum likelihood |
| fit_gau | Fit Gaussian antedependence model by maximum likelihood |
| fit_inad | Fit INAD antedependence model by maximum likelihood |
| labor_force_cat | Labor force longitudinal categorical data (Table 1) |
| logL_cat | Log-likelihood for categorical AD models (with missing data... |
| logL_gau | Log-likelihood for Gaussian AD models (with missing data... |
| logL_inad | Log-likelihood for INAD models (with missing data support) |
| logL_inad_i | INAD log likelihood contribution at time i (no fixed effect) |
| partial_corr | Compute intervenor-adjusted partial correlation matrix |
| plot_prism | PRISM plot (Partial Residual Intervenor Scatterplot Matrix) |
| plot_profile | Profile plot (spaghetti plot) for longitudinal data |
| print.cat_ci | Print method for cat_ci objects |
| print.cat_fit | Print method for cat_fit objects |
| print.cat_lrt | Print method for cat_lrt objects |
| print.gau_bic_order | Print method for BIC order selection |
| print.gau_ci | Print method for AD confidence intervals |
| print.gau_contrast_test | Print method for AD contrast test |
| print.gau_fit | Print method for gau_fit objects |
| print.gau_homogeneity_test | Print method for AD homogeneity test |
| print.gau_mean_test | Print method for AD mean test |
| print.gau_order_test | Print method for AD order test |
| print.homogeneity_tests_inad | Print method for homogeneity_tests_inad |
| print.inad_ci | Print method for INAD confidence intervals |
| print.inad_fit | Print method for INAD model fits |
| print.test_homogeneity_inad | Print method for test_homogeneity_inad |
| race_100km | 100km race split-time data |
| run_homogeneity_tests_inad | Run all homogeneity tests for INAD |
| run_order_tests_cat | Run all pairwise order tests |
| run_stationarity_tests_cat | Run all stationarity-related tests for categorical AD |
| run_stationarity_tests_gau | Run all stationarity-related tests for Gaussian AD |
| run_stationarity_tests_inad | Run all stationarity-related tests for INAD |
| simulate_cat | Simulate categorical antedependence series |
| simulate_gau | Simulate Gaussian antedependence series |
| simulate_inad | Simulate INAD antedependence series |
| summary.cat_ci | Summary method for cat_ci objects |
| summary.gau_ci | Summary method for gau_ci objects |
| summary.inad_ci | Summary method for inad_ci objects |
| test_contrast_gau | Test linear hypotheses on the mean under antedependence |
| test_homogeneity_cat | Likelihood ratio test for homogeneity across groups... |
| test_homogeneity_gau | Likelihood ratio test for homogeneity across groups (Gaussian... |
| test_homogeneity_inad | Likelihood ratio test for homogeneity across groups (INAD... |
| test_one_sample_gau | One-sample test for mean structure under antedependence |
| test_order_cat | Likelihood ratio test for antedependence order (categorical... |
| test_order_gau | Likelihood ratio test for antedependence order (Gaussian AD... |
| test_order_inad | Likelihood ratio test for antedependence order (INAD data) |
| test_stationarity_cat | Likelihood ratio test for stationarity (categorical AD data) |
| test_stationarity_gau | Likelihood ratio test for stationarity (Gaussian AD data) |
| test_stationarity_inad | Likelihood ratio test for stationarity (INAD data) |
| test_timeinvariance_cat | Likelihood ratio test for time-invariance (categorical data) |
| test_two_sample_gau | Two-sample test for equality of mean profiles under... |
