Agresti_bisection       Solves equation Agresti_f() = 0 for delta by
                        method of bisection..
Agresti_compute_lambda
                        Computes value of lambda parameter
Agresti_compute_pi      Computes the matrix pi of model-based
                        proportions
Agresti_create_design_matrix
                        Creates the design matrix for Agresti's simple
                        diagonal quasi-symmetry model.
Agresti_equation_1      First equation in section 3. Solved for kappa.
Agresti_equation_2      Second equation in section 3. Solved for
                        pi_margin.
Agresti_equation_3      Third equation in section 3. Solved for lambda
Agresti_extract_delta   Extracts the quasi-symmetry information from
                        the result provided.
Agresti_f               Function value for first equation in section 3.
Agresti_kappa_agreement
                        Fits Agresti's agreement model that includes
                        kappa as a parameter.
Agresti_simple_diagonals_parameter_quasi_symmetry
                        Agresti's simple diganal quasi-symmetry model.
Agresti_starting_values
                        Computes staring values for marginal pi.
Agresti_w_diff          Computes the weighted statistics listed in
                        section 2.3.
Agresti_weighted_tau    Computes weighted tau from Section 2.1.
                        Agresti, A. (1983). Testing marginal
                        homogeneity for ordinal categorical variables.
                        Biometrics, 39(2), 505-510.
Bhapkar_marginal_homogeneity
                        Bhapkar's (1979) test for marginal homogeneity
Bhapkar_quasi_symmetry
                        Bhapkar's 1979 test for quasi-symmetry.
Bowker_symmetry         Computes Bowker's test of symmetry.
Clayton_marginal_location
                        Fits the tests comparing locations of the
                        margins of a two-way table.
Clayton_stratified_marginal_location
                        Clayton's stratified version of the marginal
                        location comparison.
Clayton_summarize       Computes summary, cumulative proportions up to
                        index provided
Clayton_summarize_stratified
                        Analysis stratified by column variable j.
Clayton_two_way_association
                        Clayton's stratified measure of association
Cliff_as_d_matrix       Converts two vectors containing scores and
                        integer frequencies (cell counts) into a
                        d-matrix
Cliff_compute_d         Computes between groups dominance matrix "d".
Cliff_counts_2          Generates counts from table frequencies for 2
                        category items
Cliff_counts_3          Generates counts from table frequencies for 3
                        category items
Cliff_counts_4          Generates counts from table frequencies for 4
                        category items
Cliff_counts_5          Generates counts from table frequencies for 5
                        category items
Cliff_counts_6          Generates counts from table frequencies for 6
                        category items
Cliff_dependent         Computes Cliff's dependent d-statistics based
                        on a dominance matrix.
Cliff_dependent_compute_cov
                        Computes sum term in covariance db-dw for
                        weighted dominance matrix.
Cliff_dependent_compute_cov_from_d
                        Compute the sum in the covariance of db+dw
Cliff_dependent_compute_from_matrix
                        Computes Cliff's dependent d-statistics based
                        on a dominance matrix.
Cliff_dependent_compute_from_table
                        Computes Cliff's dependent d-statistics based
                        on a table of frequency counts.
Cliff_dependent_compute_paired_d
                        Computes Cliff's dependent d-statistics based
                        on cell frequencies.
Cliff_independent       Computes the independent groups d-statistic
                        comparing the two vectors provided.
Cliff_independent_from_matrix
                        Computes d-statistic from dominance matrix
                        provided.
Cliff_independent_from_table
                        Computes independent group's d-statistic from
                        the matrix of frequencies provided.
Cliff_independent_weighted
                        Computes d-statistic based on scores and
                        integer weights(frequencies) for each group.
Cliff_weighted_d_matrix
                        Computes weighted version of dominance matrix
                        "d"
Goodman_constrained_diagonals_parameter_symmetry
                        Fits the model where some of the delta
                        parameters are constrained to be equal to one
                        another.
Goodman_diagonals_parameter_symmetry
                        Fit's Goodman's diagonals parameter symmetry
                        model.
Goodman_fixed_parameter
                        Fits the model with given parameters fixed to
                        specific values.
Goodman_ml              Performs ML estimation of the model.
Goodman_model_i         Fits Goodman's (1979) Model I
Goodman_model_i_star    Fits Goodman's (1979) Model I*
Goodman_model_ii        Fits Goodman's (1979) Model II
Goodman_model_ii_star   Fits Goodman's (1979) model II*, where row and
                        column effects are equal.
Goodman_null_association
                        Fits Goodman's L. A. (1979) Simple Models for
                        the Analysis of Association in
                        Cross-Classifications Having Ordered Categories
Goodman_pi              Computes the model-based probability for cell
                        i, j
Goodman_pi_matrix       Computes the full matrix of model-based cell
                        probabilities.
Goodman_symmetric_association_model
                        Fits the symmetric association model from
                        Goodman (1979). Note the model is a
                        reparameterized version of the quasi-symmetry
                        model, so the quasi-symmetry model has the same
                        fit indices.
Goodman_uniform_association
                        Fits Goodman's (1979) uniform association model
Ireland_marginal_homogeneity
                        Fits marginal homogeneity model
Ireland_mdis            Computes the MDIS between the two matrices
                        provided.
Ireland_normalize_for_truncation
                        Renormalize counts to account for truncation of
                        diagonal
Ireland_quasi_symmetry
                        Fit for quasi-symmetry model. Obtained by
                        subtraction, so no model-based probabilities.
Ireland_quasi_symmetry_model
                        Fitss the quasi-symmetry model.
Ireland_symmetry        Fits symmetry model.
McCullagh_compute_Nij   Compute the observed sums Nij
McCullagh_compute_c_plus
                        Computes sums c+ used in maximizing the
                        log(likelihod)
McCullagh_compute_condition
                        Compute the linear constraint on psi elements
                        for identifiablity.
McCullagh_compute_cumulative_sums
                        Computes cumulative sums for rows,
McCullagh_compute_cumulatives
                        Computes the model-based cumulative probability
                        matrices pij and qij
McCullagh_compute_df    Computes the degrees of freedom for the model
McCullagh_compute_gamma
                        Computes gamma from x and beta
McCullagh_compute_gamma_from_phi
                        Computes value of gamma from phi. Inverse of
                        usual computation.
McCullagh_compute_gamma_plus_1_from_phi
                        Computes value of gamma[j + 1] from phi.
McCullagh_compute_generalized_cumulatives
                        Coompute the model-based cumulative
                        probabilities pij and qij.
McCullagh_compute_generalized_pi
                        Cpompute matrix pi under generalized model.
McCullagh_compute_lambda
                        Computes lambda, log of cumulative odds.
McCullagh_compute_log_l
                        Computes the log(likelihood) for the general
                        nonlinear model.
McCullagh_compute_omega
                        Compute the value of the Lagrange multiplier
                        for the constraint on psi.
McCullagh_compute_phi   Computes phi based on gamma
McCullagh_compute_phi_matrix
                        Compute matrix of model-based logits
McCullagh_compute_pi    Compute the regular (non-cumulative)
                        model-based pi values
McCullagh_compute_pi_from_beta
                        Computes matrix of p-values pi based on x and
                        current value of beta.
McCullagh_compute_pi_from_gamma
                        Compute the cell probabilities pi from gamma.
McCullagh_compute_regression_weights
                        Computes regression weights w; R_dot_j * (N -
                        R_dot_j[j]) * (n_do_j[j] a= na_dot_j[j+ 1] )
McCullagh_compute_s_plus
                        Compute sums too use in maximizing
                        log(likelihood)
McCullagh_compute_update
                        Compute the Newton-Raphson update.
McCullagh_compute_z     Computes Z, where z is w * lambda.
McCullagh_conditional_symmetry
                        Fits the McCullagh (1978) conditional-symmetry
                        model.
McCullagh_conditional_symmetry_compute_s
                        Computes sums used in maximizing theta.
McCullagh_conditional_symmetry_initialize_phi
                        Initializes symmetry matrix phi
McCullagh_conditional_symmetry_maximize_phi
                        Maximizes log(likelihood) wrt phi.
McCullagh_conditional_symmetry_maximize_theta
                        Maximizes the log(likelihood) wrt theta.
McCullagh_conditional_symmetry_pi
                        Computes model-based proportions.
McCullagh_derivative_condition_wrt_psi
                        Derivative of the condition wrt psi[i, j].
McCullagh_derivative_gamma_plus_1_wrt_phi
                        Derivative of gamma j + 1 wrt phi.
McCullagh_derivative_gamma_wrt_phi
                        Derivative of gamma wrt phi.
McCullagh_derivative_gamma_wrt_y
                        Derivative of y wrt gamma.
McCullagh_derivative_lagrangian_wrt_delta
                        Derivative of Lagrange multiplier wrt scalar
                        delta.
McCullagh_derivative_lagrangian_wrt_delta_vec
                        Derivative of Lagrangian wrt delta_vec.
McCullagh_derivative_lagrangian_wrt_psi
                        Derivative of Lagrangian wrt psi[i1, j1].
McCullagh_derivative_log_l_wrt_alpha
                        Derivative of log(likelihood) wrt alpha[index].
McCullagh_derivative_log_l_wrt_beta
                        Derivative of log(likelihood) wrt beta, as
                        given in appendix of McCullagh.
McCullagh_derivative_log_l_wrt_c
                        Derivative of log(likelihood) wrt c.
McCullagh_derivative_log_l_wrt_delta
                        Derivative of log(likelihood) wrt delta (scalar
                        or vector0.
McCullagh_derivative_log_l_wrt_delta_vec
                        Derivative of log(likelihood) wrt delta_vec[k].
McCullagh_derivative_log_l_wrt_params
                        Derivative of log(likelihood) wrt parameters.
McCullagh_derivative_log_l_wrt_phi
                        Derivative of log(likelihood) wrt phi[i, j]
McCullagh_derivative_log_l_wrt_psi
                        Derivative of log(likelihood) wrt psi.
McCullagh_derivative_omega_wrt_alpha
                        Derivative of Lagrange multiplier omega wrt
                        alpha[index].
McCullagh_derivative_omega_wrt_c
                        Derivative of Lagrange multiplier omega wrt c.
McCullagh_derivative_omega_wrt_delta
                        Derivative of Lagrange multiplier omega wrt
                        scalar delta.
McCullagh_derivative_omega_wrt_delta_vec
                        Derivative of Lagrange multiplier omega wrt
                        vector delta[k].
McCullagh_derivative_omega_wrt_psi
                        Derivative of Lagrange multiplier omega wrt
                        psi[i, j].
McCullagh_derivative_phi_wrt_gamma
                        Derivative of phi wrt gamma.
McCullagh_derivative_pi_wrt_alpha
                        Derivative of pi[i, j] wrt alpha[index].
McCullagh_derivative_pi_wrt_c
                        Derivative pi[i, j] wrt c.
McCullagh_derivative_pi_wrt_delta
                        Derivative of pi[i, j] wrt delta.
McCullagh_derivative_pi_wrt_delta_vec
                        Derivative pi[i, j] wrt delta[k].
McCullagh_derivative_pi_wrt_psi
                        Derivative of pi[i, j] wrt psi[i1, j1].
McCullagh_derivative_pij_wrt_alpha
                        Derivative of pij[i, j] wrt alpha[index]
McCullagh_derivative_pij_wrt_c
                        Derivative pij[i, j] wrt c.
McCullagh_derivative_pij_wrt_delta
                        Derivative of pij[i, j] wrt scalar delta.
McCullagh_derivative_pij_wrt_delta_vec
                        Derivative pij[i,j] wrt vector delta[k].
McCullagh_derivative_pij_wrt_psi
                        Derivative of pij[a, b] wrt psi[h, k]
McCullagh_extract_weights
                        Extracts the weights to convert cumulative
                        model-based probabilities to regular
                        probabilities.
McCullagh_fit_location_regression_model
                        Fit location model
McCullagh_generalized_palindromic_symmetry
                        Generalized version of palindromic symmetry
                        model
McCullagh_generalized_pij_qij
                        Computes culuative model probabilities for the
                        generalized model using vector delta.
McCullagh_generate_names
                        Generates names to label the parameters.
McCullagh_get_statistics
                        Computes summary statistics needed to compute
                        estimate of delta.
McCullagh_gradient_log_l
                        Gradient vector of log(likelihood)
McCullagh_hessian_log_l
                        Hessian matrix of log(likelihood)
McCullagh_initialize_beta
                        Initializes the beta vector.
McCullagh_initialize_delta
                        Compute initial values for scalar delta
McCullagh_initialize_delta_vec
                        Initialize vector delta
McCullagh_initialize_psi
                        Initialize the symmetry matrix psi
McCullagh_initialize_x
                        Initialize design matrix for location model.
McCullagh_is_in_constraint_set
                        Logical test of whether a specific psi will be
                        in the constraint set.
McCullagh_is_pi_invalid
                        Test whether pi matrix is valid, i.e., 0 < all
                        values.
McCullagh_log_L         Computes the log(likelihood).
McCullagh_logistic_model
                        MCCullagh's logistic model.
McCullagh_logits        Computed cumulative logits.
McCullagh_maximize_q_symmetry
                        Maximize the log(likelihood) wrt parameters phi
                        and alpha
McCullagh_newton_raphson_update
                        Newton-Raphson update.
McCullagh_palindromic_symmetry
                        McCullagh's palindromic symmetry model
McCullagh_penalized     Computes the penalized value of a derivative by
                        adding the derivative of the penalty to it.
McCullagh_pij_qij       Compute model-based cumulative probabilities
McCullagh_proportional_hazards
                        Computes the proportional hazards.
McCullagh_q_symmetry_initialize_alpha
                        Initializes the asymmetry vector alpha
McCullagh_q_symmetry_initialize_phi
                        Initializes the phi matrix
McCullagh_q_symmetry_pi
                        Computes the model-based p-values
McCullagh_quasi_symmetry
                        Fits McCullagh's (1978) quasi-symmetry model.
McCullagh_second_order_lagrangian_wrt_psi_2
                        Second derivative of Lagrangian wrt psi^2.
McCullagh_second_order_lagrangian_wrt_psi_alpha
                        Second derivative of Lagrangian wrt psi[i1, j1]
                        and alpha[index].
McCullagh_second_order_lagrangian_wrt_psi_delta
                        Second derivative of Lagrangian wrt psi[i1, j1]
                        and delta.
McCullagh_second_order_lagrangian_wrt_psi_delta_vec
                        Second derivative of Lagrangian wrt psi[i1, j1]
                        and delta_vec[k[.
McCullagh_second_order_log_l_wrt_alpha_2
                        Second derivative of log(likelihood) wrt
                        alpha^2.
McCullagh_second_order_log_l_wrt_alpha_c
                        Second derivative of log(likelihood) wrt
                        alpha[index] and c.
McCullagh_second_order_log_l_wrt_beta_2
                        Expected values of second order derivatives of
                        log(likelihood) wrt beta.
McCullagh_second_order_log_l_wrt_c_2
                        Second derivative of log(likelihood) wrt c^2.
McCullagh_second_order_log_l_wrt_delta_2
                        Second derivative of log(likelihood) wrt
                        delta^2.
McCullagh_second_order_log_l_wrt_delta_alpha
                        Second derivative of log(likelihood) wrt delta
                        and alpha[index].
McCullagh_second_order_log_l_wrt_delta_c
                        Second derivative of log(likelihood) wrt scalar
                        delta and c.
McCullagh_second_order_log_l_wrt_delta_vec_2
                        Second derivative of log(likelihood) wrt
                        delta_vec^2.
McCullagh_second_order_log_l_wrt_delta_vec_alpha
                        Second derivative of log(likelihood) wrt
                        delta[k] and alpha[index].
McCullagh_second_order_log_l_wrt_delta_vec_c
                        Second derivative of log(likeloihood) wrt
                        delta_vec[k] and c.
McCullagh_second_order_log_l_wrt_parms
                        Expected second order derivatives of
                        log(likelihood)
McCullagh_second_order_log_l_wrt_psi_2
                        Second derivative of log(likelihoood) wrt
                        psi^2.
McCullagh_second_order_log_l_wrt_psi_alpha
                        Second derivative of log(likelihoood) wrt
                        ps[i1, j1] and alpha[index].
McCullagh_second_order_log_l_wrt_psi_c
                        Second derivative of log(likelihood) wrt
                        psi[i1, j1] and c.
McCullagh_second_order_log_l_wrt_psi_delta
                        Second derivative of log(likelihood) wrt
                        psi[i1, j1] and scalar delta..
McCullagh_second_order_log_l_wrt_psi_delta_vec
                        Second derivative of log(likelihood) wrt
                        psi[i1, j1] and delta_vec[k].
McCullagh_second_order_omega_wrt_alpha_2
                        Second derivative of Lagrange multiplier omega
                        wrt alpha^2.
McCullagh_second_order_omega_wrt_alpha_c
                        Second derivative of Lagrange multiplier omega
                        wrt alpha[index] and c.
McCullagh_second_order_omega_wrt_c_2
                        Second derivative of Lagrange multiplier omega
                        wrt c^2.
McCullagh_second_order_omega_wrt_delta_2
                        Second derivative of Lagrange multiplier omega
                        wrt scalae delta^2.
McCullagh_second_order_omega_wrt_delta_alpha
                        Second derivative of Lagrange multiplier omega
                        wrt delta and alpha[index].
McCullagh_second_order_omega_wrt_delta_c
                        Second derivative of Lagrange multiplier omega
                        wrt scalar delta and c.
McCullagh_second_order_omega_wrt_delta_vec_2
                        Second derivative of Lagrange multiplier omega
                        wrt delta_vec^2.
McCullagh_second_order_omega_wrt_delta_vec_alpha
                        Second derivative of Lagrange multiplier omega
                        wrt delta_vec[k] and alpha[index].
McCullagh_second_order_omega_wrt_delta_vec_c
                        Second derivative of Lagrange multiplier omega
                        wrt delta_vec[k] and c.
McCullagh_second_order_omega_wrt_psi_2
                        Second derivative of Lagrange multiplier omega
                        wrt psi^2.
McCullagh_second_order_omega_wrt_psi_alpha
                        Second derivative of Lagrange multiplier omega
                        wrt psi[i1, j1] and alpha[index].
McCullagh_second_order_omega_wrt_psi_c
                        Second derivative of Lagrange multiplier omega
                        wrt psi[i1, j1] and c.
McCullagh_second_order_omega_wrt_psi_delta
                        Second derivative of Lagrange multiplier omega
                        wrt psi and scalar delta.
McCullagh_second_order_omega_wrt_psi_delta_vec
                        Second derivative of Lagrange multiplier omega
                        wrt psi[i1, j1] and delta_vec[k].
McCullagh_second_order_pi_wrt_alpha_2
                        Second derivative of pi[i, j] wrt alpha^2.
McCullagh_second_order_pi_wrt_alpha_c
                        Second derivaitve of pi[i, j] wrt alpha[index]
                        and c.
McCullagh_second_order_pi_wrt_c_2
                        Second order derivative of pi[i, j] wrt c^2.
McCullagh_second_order_pi_wrt_delta_2
                        Second order derivative of pi[i, j] wrt scalar
                        delta.
McCullagh_second_order_pi_wrt_delta_alpha
                        Second order deriviative of pi[i, j] wrt scalar
                        delta and alpha[index]
McCullagh_second_order_pi_wrt_delta_c
                        Second order derivative of pi[i, j] wrt scalae
                        delta and c.
McCullagh_second_order_pi_wrt_delta_vec_2
                        Derivative of pi[i, j] wrt delta^2.
McCullagh_second_order_pi_wrt_delta_vec_alpha
                        Second order dertivative of pi[i, j] wrtt
                        delta[k] alpha[index].
McCullagh_second_order_pi_wrt_delta_vec_c
                        Second derivative of pi[i, j] wrt delta[k] and
                        c.
McCullagh_second_order_pi_wrt_psi_2
                        Second order derivative wrt psi^2.
McCullagh_second_order_pi_wrt_psi_alpha
                        Second order derivative of pi[i, j] wrt psi[i1,
                        j1] and alpha[index].
McCullagh_second_order_pi_wrt_psi_c
                        Second order derivative of pi[i, j] wrt psi[i1,
                        j1] and c.
McCullagh_second_order_pi_wrt_psi_delta
                        Second order derivaitve of pi wrt pshi and
                        scalar delta.
McCullagh_second_order_pi_wrt_psi_delta_vec
                        Second order derivaitve of pi[i, j] wrt psi[i1,
                        j1] and kelta[k].
McCullagh_update_parameters
                        Update the parameters based on Newton-Raphson
                        step.
McCullagh_v_inverse     Compute v_inverse (from appendix).
Schuster_compute_df     Computes the degrees of freedom for the model.
Schuster_compute_pi     Compute matrix of model-based proportions pi.
Schuster_compute_starting_values
                        Computes starting values for the model.
Schuster_derivative_log_l_wrt_kappa
                        Derivative of log(likelihood) wrt kappa.
Schuster_derivative_log_l_wrt_marginal_pi
                        Derivative of log(likelihood) wrt
                        marginal_pi[k]
Schuster_derivative_log_l_wrt_v
                        Derivative of log(likelihood) wrt v[i1, j1]
Schuster_derivative_pi_wrt_kappa
                        Derivative of pi[i, j] wrt kappa coefficient.
Schuster_derivative_pi_wrt_marginal_pi
                        Derivative of pi[i, j] wrt marginal_pi[k].
Schuster_derivative_pi_wrt_v
                        Computes derivative of pi[i, j] wrt v[i1, j1]
Schuster_derivative_v_wrt_v
                        Computes derivative of v[i1, j1] wrt v[i2, j2]
Schuster_enforce_constraints_on_v
                        Compute v matrix subject to constraints on rows
                        1..r-1.
Schuster_gradient       Gradient vector log(L) wrt parameters.
Schuster_hessian        Computes the hessian matrix of second-order
                        partial derivatives of log(L).
Schuster_is_pi_valid    Determines whether the candidate pi matrix is
                        valid.
Schuster_newton_raphson
                        Performs Newton-Raphson step.
Schuster_second_deriv_log_l_wrt_kappa_2
                        Second order partial log(L) wrt kappa^2.
Schuster_second_deriv_log_l_wrt_kappa_v
                        Second order partial log(L) wrt kappa and v.
Schuster_second_deriv_log_l_wrt_marginal_pi_2
                        Second order partial log(L) wrt marginal_pi^2.
Schuster_second_deriv_log_l_wrt_marginal_pi_kappa
                        Second order partial log(L) wrt marginal_pi and
                        kappa.
Schuster_second_deriv_log_l_wrt_marginal_pi_v
                        Second order partial log(L) wrt marginal_pi and
                        v.
Schuster_second_deriv_log_l_wrt_v_2
                        Second order partial log(L) wrt v^2.
Schuster_second_deriv_pi_wrt_kappa_2
                        Second order partial wrt kappa, kappa
Schuster_second_deriv_pi_wrt_kappa_v
                        Second order partial wrt kappa, v
Schuster_second_deriv_pi_wrt_marginal_pi_2
                        Second derivative of pi[i, j] wrt
                        marginal_pi[k]^2
Schuster_second_deriv_pi_wrt_marginal_pi_kappa
                        Second order partial wrt kappa, marginal_pi
Schuster_second_deriv_pi_wrt_marginal_pi_v
                        Second order partial pi wrt marginal_pi and v
Schuster_second_deriv_pi_wrt_v_2
                        Second order partial wrt v^2
Schuster_solve_for_v    Solves for the last row and diagonal of
                        symmetry matrix v (v-tilde) using constraint
                        equations
Schuster_solve_for_v1   Solves for the last row and diagonal of
                        symmetry matrix v (parameteer v-tilde) using
                        linear algebra formulation from paper.
Schuster_symmetric_rater_agreement_model
                        Computes the model that has kappa as a
                        coefficient and symmetry.
Schuster_update         Computes the Newton-Raphson update
Schuster_v_tilde        Computes the common diagonal term v-tilde.
Stuart_marginal_homogeneity
                        Computes Stuart's Q test of marginal
                        homogeneity.
budget_actual           Participation in household budgeting by
                        psychiatric patients. Rows are ratings by
                        patient, columns are ratings by relative. 1 -
                        not at all 2 - doing some 3 - doing regularly
budget_expected         Ratings of expected participation in household
                        budgeting by psychiatric patients. Rows are
                        ratings by patient, columns are ratings by
                        relative. 1 - not at all 2 - doing some 3 -
                        doing regularly
coal_g                  Degree of disease measured at two points in
                        time for mine workers.
constant_of_integration
                        Computes the constant of integration of a
                        multinomial sample.
depression              Ratings of severity of patient's depression by
                        two therapists.
dogs                    Dehydration in dogs data set.
dreams                  Severity of disturbing dreams in adolescent
                        boys, measured at two ages..
dumping                 Occurrence of side effects after
                        gastro-intestinal surgery.
esophageal_cancer       Ratings of number of hot drinks consumed by
                        cases with cancer of the esophagus, compared
                        with control subjects.
expand                  Converts weighted (x, w) pairs into unweighted
                        data by replicating x[i] w[i] times
expit                   Computes the "expit" function - inverse of
                        logit.
family_income           Family income for two years from US census.
gender_vision           Ratings of visual acuity for men and women
                        employed at the Royal Ordinance factories,
                        1943-1946.
handle_max_i_i          Case where j == r, i == k == k2
handle_max_i_k          Case where j == r, i != k, i == k2
handle_max_k_k2         Case where j == r, i != k && i != k2
handle_one_maximum      Case where pi[i, r] with k and k2
handle_tied_below_maximum
                        Case where i == j, i < r, j < r
handle_tied_maximum     Case where pi[r, r] with k and k2
handle_untied_below_maximum
                        Case where i != j, i < r && j < r
homicide_black_black    Data about charges of homicide in the state of
                        Florida.
homicide_black_white    Data about charges of homicide in the state of
                        Florida.
homicide_white_black    Data about charges of homicide in the state of
                        Florida.
homicide_white_white    Data about charges of homicide in the state of
                        Florida.
hypothalamus_1          Measures of men's hypothalamus taken from
                        cadavers.  First data set.
hypothalamus_2          Measures of men's hypothalamus taken from
                        cadavers. Second data set.
interference_12         Measures of interference in memory recall
                        study.
interference_control_1
                        Measures of interference in memory recall
                        study.
interference_control_2
                        Measures of interference in memory recall
                        study.
is_invertible           Tests whether a square matrix is invertible
                        (non singular)
is_missing_or_infinite
                        Determines if its argument is not a valid
                        number.
kappa                   Computes Cohen's 1960 kappa coefficient
likelihood_ratio_chisq
                        Computes the likelihood ratio G^2 measure of
                        fit.
loadRData               Function to load a data set written out using
                        save().
log_Linear_create_log_n
                        Computes the logs of the cell frequencies.
log_likelihood          Computes the multinomial log(likelihood).
log_linear_add_all_diagonals
                        Adds indicator variables for the diagonal cells
                        in table n.
log_linear_append_column
                        Appends a column to an existing design matrix.
log_linear_create_coefficient_names
                        Creates missing column names
log_linear_create_linear_by_linear
                        Creates a vector containing the
                        linear-by-linear vector.
log_linear_equal_weight_agreement_design
                        Creates design matrix for model with main
                        effects and a single agreement parameter delta.
log_linear_fit          Fits a log-linear model to the data provided,
                        using the design matrix provided. Names for the
                        effects will be "rows1", "cols1" etc.  If there
                        are remaining entries, they can be specified as
                        the "effect_names" character vector. This
                        function is a wrapper around a call to glm()
                        that handles some of the details of the call
                        and packages the output in a more convenient
                        form.
log_linear_main_effect_design
                        Design matrix for baseline independence model
                        with main effects for rows and columns.
log_linear_matrix_to_vector
                        Converts a matrix of data into a vector
                        suitable for use in analysis with the design
                        matrices created. Unlike simply calling
                        vector() on the matrix the resulting vector is
                        organized by rows, then columns. This order
                        corresponds to the order in the design matrix.
log_linear_quasi_symmetry_model_design
                        Creates the design matrix for a quasi-symmetry
                        design
log_linear_remove_column
                        Removes a column from an existing design
                        matrix.
log_linear_symmetry_design
                        Creates design matrix for symmetry model.
logit                   Computes the log-odds (logit) for the value
                        provided
mental_health           Relationship between child's mental health and
                        parents' socioeconomic status.
model_i_column_theta    Computes the column association values
                        theta-hat
model_i_effects         Gets the overall effects for Model I.
model_i_fHat            Computes model-based expected cell counts for
                        Model I
model_i_normalize_fHat
                        Normalizes pi(fHat) to sum to 1.0. If
                        exclude_diagonal is TRUE, the sum of the
                        off-diagonal terms sums to 1.0.
model_i_row_column_odds_ratios
                        Computes the table of adjacent odds-ratios
                        theta-hat.
model_i_row_theta       Computes the row association values theta-hat
model_i_star_effects    Gets the Model I* effects.
model_i_star_fHat       Computes expected frequencies for Model I*
model_i_star_update_theta
                        Updates the row/column parameters for Model I*.
model_i_starting_values
                        Computes crude starting values for Model I.
model_i_update_alpha    Updates the estimate of the alpha vector for
                        Model I
model_i_update_beta     Updates the estimate of the beta vector for
                        Model I
model_i_update_delta    Updates the estimate of the delta vector for
                        Model I
model_i_update_gamma    Updates the estimate of the gamma vector for
                        Model I
model_i_zeta            Computes the overall association theta and the
                        row and column effects zeta
model_ii_effects        Gets the effects phi, ksi_i_dot and ksi_dot_j
                        for Model II results.
model_ii_fHat           Computes expected counts for Model II
model_ii_ksi            Gets the effects phi, ksi_i_dot and ksi_dot_j
                        for Model II matrix of odds-ratios.
model_ii_star_effects   Gets the effects for Model II*
model_ii_star_fHat      Computes expected counts for Model II*
model_ii_star_update_phi
                        Updates estimate of phi vector
model_ii_starting_values
                        Computes crude starting values for Model II
model_ii_update_alpha   Updates the estimate of the alpha vector for
                        Model II
model_ii_update_beta    Updates the estimate of the beta vector for
                        Model II
model_ii_update_rho     Updates the estimate of the rho vector for
                        Model II
model_ii_update_sigma   Updates the estimate of the sigma vector for
                        Model II
movies                  Movie ratings by two film critics, Siskel and
                        Ebert.
new_orleans_data        Agreement between two clinicians on presence of
                        multiple sclerosis based on file.
null_association_fHat   Computes expected counts for null association
                        model
occupational_status     Cross tabulation of father's employment status
                        with son's employment status.
paranoia                Interrater agreement of two psychologists'
                        ratings of paranoia.
pearson_chisq           Computes the Pearson X^2 statistic.
radiology               Interrater agreement of two radiologists
                        diagnosis of severity of carcinoma.
social_status           Social mobility data with father's occupational
                        social status and son's occupational social
                        status.
social_status2          Social mobility data with father's occupational
                        social status and son's occupational social
                        status. * categories instead of 7 in social
                        status..
taste                   Taste ratings
teachers                Teachers ratings of their students
                        intelligence.
teaching_style          Style of teachers rated by supervisors
tonsils                 Relationship between size of child's tonsils
                        and their status as a carrier of a disease.
tv                      Interrater agreement of two journalists'
                        evaluation of proposed TV programs.
uniform_association_fHat
                        Computes expected counts for uniform
                        association model
uniform_association_update_theta
                        Updates estimate of theta value of the uniform
                        association model
var_kappa               Computes the sampling variance of kappa.
var_weighted_kappa      Computes the sampling variance of weighted
                        kappa.
vision_data             Visual acuity of women factory workers.
vision_data_men         Visual acuity of men factory workers.
von_Eye_diagonal        Fits the diagonal effects model, where each
                        category has its own parameter delta[k].
von_Eye_diagonal_linear_by_linear
                        Fits the diagonal effects model, where each
                        category has its own parameter delta[k], while
                        also incorporating a linear-by-linear term.
von_Eye_equal_weight_diagonal_linear
                        Fits the diagonal effects model, where there is
                        a single delta parameter for all categories,
                        while also incorporating a linear-by-linear
                        term.
von_Eye_equal_weighted_diagonal
                        Fits the equal weighted diagonal model, where
                        the diagonals all have an additional parameter
                        delta, with the constraint that delta is equal
                        across all categories.
von_Eye_linear_by_linear
                        Fits the basic independent rows and columns
                        model incorporating a linear-by-linear term.
von_Eye_main_effect     Fits the base model with only independent row
                        and column effects.
von_Eye_weight_by_response_category_design
                        Creates design matrix for weight be response
                        category model.
weighted_cov            Computes the weighted covariance
weighted_kappa          Computes Cohen's 1968 weighted kappa
                        coefficient
weighted_var            Computes the weighted variance
winnipeg_data           Agreement between two clinicians on presence of
                        multiple sclerosis based on file.
