Package index
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D_matrix_contribution() - Compute D matrix contribution for a given cluster to robust score statistic for glm robust score tests.
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S_matrix_contribution() - Compute S matrix contribution for a given cluster to robust score statistic for glm robust score tests.
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V_matrix_contribution() - Compute V matrix contribution to robust score statistic for glm robust score tests.
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combine_independent_p_values() - Combine independent p-values that test the same null hypothesis into an overall summary
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fisher_info_contribution() - Compute fisher information contribution to robust score statistic for glm robust score tests.
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gee_test() - Generalized Estimating Equations under technical replication with robust and non-robust Wald and Rao (score) tests
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get_test_statistic() - Just in case anyone wants to invert a p-value to recover the chi-squared distributed test statistic
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glm_test() - Generalized Linear Models with robust and non-robust Wald and Rao (score) tests
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jackknife_se() - Jackknife standard errors
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lincom() - Run robust wald test for null hypothesis of form A x beta = c
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multinom_beta_vector_to_matrix() - Create \(\beta\) matrix from vector of \(\beta\) for \(\beta_k : k \neq j\)
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multinom_fisher_scoring() - Optimization under null or alternative for multinomial model via Fisher scoring.
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multinom_get_probs() - Compute multinomial probabilities for a given value of model parameters.
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multinom_info_mat() - Compute Information matrix for model parameters.
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multinom_log_lik() - Negative log-likelihood for multinomial data under the alternative
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multinom_penalized_estimation() - Optimization under null or alternative for multinomial model with a Firth penalty.
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multinom_score_vector() - Compute score for model parameters.
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multinom_test() - Robust score (Rao) tests for multinomial regression.
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print(<raoFit>) - Print function
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robust_score_test() - Robust score (Rao) tests with finite-sample correction
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score_contribution() - Compute score contribution to robust score statistic for glm robust score tests.
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set_up_lin_com() - Create matrix to be populated with coefficients for user-specified number of hypotheses.
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simulate_data_mult() - Simulate multinomial data under the null (strong or weak) or alternative