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Arnold <jeffrey.arnold@gmail.com>\nWebsite: http://mc-stan.org/\nCategory: scientific\n*/\nfunction stan(hljs) {\n // variable names cannot conflict with block identifiers\n var BLOCKS = ['functions', 'model', 'data', 'parameters', 'quantities', 'transformed', 'generated'];\n var STATEMENTS = ['for', 'in', 'if', 'else', 'while', 'break', 'continue', 'return'];\n var SPECIAL_FUNCTIONS = ['print', 'reject', 'increment_log_prob|10', 'integrate_ode|10', 'integrate_ode_rk45|10', 'integrate_ode_bdf|10', 'algebra_solver'];\n var VAR_TYPES = ['int', 'real', 'vector', 'ordered', 'positive_ordered', 'simplex', 'unit_vector', 'row_vector', 'matrix', 'cholesky_factor_corr|10', 'cholesky_factor_cov|10', 'corr_matrix|10', 'cov_matrix|10', 'void'];\n var FUNCTIONS = ['Phi', 'Phi_approx', 'abs', 'acos', 'acosh', 'algebra_solver', 'append_array', 'append_col', 'append_row', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'bernoulli_cdf', 'bernoulli_lccdf', 'bernoulli_lcdf', 'bernoulli_logit_lpmf', 'bernoulli_logit_rng', 'bernoulli_lpmf', 'bernoulli_rng', 'bessel_first_kind', 'bessel_second_kind', 'beta_binomial_cdf', 'beta_binomial_lccdf', 'beta_binomial_lcdf', 'beta_binomial_lpmf', 'beta_binomial_rng', 'beta_cdf', 'beta_lccdf', 'beta_lcdf', 'beta_lpdf', 'beta_rng', 'binary_log_loss', 'binomial_cdf', 'binomial_coefficient_log', 'binomial_lccdf', 'binomial_lcdf', 'binomial_logit_lpmf', 'binomial_lpmf', 'binomial_rng', 'block', 'categorical_logit_lpmf', 'categorical_logit_rng', 'categorical_lpmf', 'categorical_rng', 'cauchy_cdf', 'cauchy_lccdf', 'cauchy_lcdf', 'cauchy_lpdf', 'cauchy_rng', 'cbrt', 'ceil', 'chi_square_cdf', 'chi_square_lccdf', 'chi_square_lcdf', 'chi_square_lpdf', 'chi_square_rng', 'cholesky_decompose', 'choose', 'col', 'cols', 'columns_dot_product', 'columns_dot_self', 'cos', 'cosh', 'cov_exp_quad', 'crossprod', 'csr_extract_u', 'csr_extract_v', 'csr_extract_w', 'csr_matrix_times_vector', 'csr_to_dense_matrix', 'cumulative_sum', 'determinant', 'diag_matrix', 'diag_post_multiply', 'diag_pre_multiply', 'diagonal', 'digamma', 'dims', 'dirichlet_lpdf', 'dirichlet_rng', 'distance', 'dot_product', 'dot_self', 'double_exponential_cdf', 'double_exponential_lccdf', 'double_exponential_lcdf', 'double_exponential_lpdf', 'double_exponential_rng', 'e', 'eigenvalues_sym', 'eigenvectors_sym', 'erf', 'erfc', 'exp', 'exp2', 'exp_mod_normal_cdf', 'exp_mod_normal_lccdf', 'exp_mod_normal_lcdf', 'exp_mod_normal_lpdf', 'exp_mod_normal_rng', 'expm1', 'exponential_cdf', 'exponential_lccdf', 'exponential_lcdf', 'exponential_lpdf', 'exponential_rng', 'fabs', 'falling_factorial', 'fdim', 'floor', 'fma', 'fmax', 'fmin', 'fmod', 'frechet_cdf', 'frechet_lccdf', 'frechet_lcdf', 'frechet_lpdf', 'frechet_rng', 'gamma_cdf', 'gamma_lccdf', 'gamma_lcdf', 'gamma_lpdf', 'gamma_p', 'gamma_q', 'gamma_rng', 'gaussian_dlm_obs_lpdf', 'get_lp', 'gumbel_cdf', 'gumbel_lccdf', 'gumbel_lcdf', 'gumbel_lpdf', 'gumbel_rng', 'head', 'hypergeometric_lpmf', 'hypergeometric_rng', 'hypot', 'inc_beta', 'int_step', 'integrate_ode', 'integrate_ode_bdf', 'integrate_ode_rk45', 'inv', 'inv_Phi', 'inv_chi_square_cdf', 'inv_chi_square_lccdf', 'inv_chi_square_lcdf', 'inv_chi_square_lpdf', 'inv_chi_square_rng', 'inv_cloglog', 'inv_gamma_cdf', 'inv_gamma_lccdf', 'inv_gamma_lcdf', 'inv_gamma_lpdf', 'inv_gamma_rng', 'inv_logit', 'inv_sqrt', 'inv_square', 'inv_wishart_lpdf', 'inv_wishart_rng', 'inverse', 'inverse_spd', 'is_inf', 'is_nan', 'lbeta', 'lchoose', 'lgamma', 'lkj_corr_cholesky_lpdf', 'lkj_corr_cholesky_rng', 'lkj_corr_lpdf', 'lkj_corr_rng', 'lmgamma', 'lmultiply', 'log', 'log10', 'log1m', 'log1m_exp', 'log1m_inv_logit', 'log1p', 'log1p_exp', 'log2', 'log_determinant', 'log_diff_exp', 'log_falling_factorial', 'log_inv_logit', 'log_mix', 'log_rising_factorial', 'log_softmax', 'log_sum_exp', 'logistic_cdf', 'logistic_lccdf', 'logistic_lcdf', 'logistic_lpdf', 'logistic_rng', 'logit', 'lognormal_cdf', 'lognormal_lccdf', 'lognormal_lcdf', 'lognormal_lpdf', 'lognormal_rng', 'machine_precision', 'matrix_exp', 'max', 'mdivide_left_spd', 'mdivide_left_tri_low', 'mdivide_right_spd', 'mdivide_right_tri_low', 'mean', 'min', 'modified_bessel_first_kind', 'modified_bessel_second_kind', 'multi_gp_cholesky_lpdf', 'multi_gp_lpdf', 'multi_normal_cholesky_lpdf', 'multi_normal_cholesky_rng', 'multi_normal_lpdf', 'multi_normal_prec_lpdf', 'multi_normal_rng', 'multi_student_t_lpdf', 'multi_student_t_rng', 'multinomial_lpmf', 'multinomial_rng', 'multiply_log', 'multiply_lower_tri_self_transpose', 'neg_binomial_2_cdf', 'neg_binomial_2_lccdf', 'neg_binomial_2_lcdf', 'neg_binomial_2_log_lpmf', 'neg_binomial_2_log_rng', 'neg_binomial_2_lpmf', 'neg_binomial_2_rng', 'neg_binomial_cdf', 'neg_binomial_lccdf', 'neg_binomial_lcdf', 'neg_binomial_lpmf', 'neg_binomial_rng', 'negative_infinity', 'normal_cdf', 'normal_lccdf', 'normal_lcdf', 'normal_lpdf', 'normal_rng', 'not_a_number', 'num_elements', 'ordered_logistic_lpmf', 'ordered_logistic_rng', 'owens_t', 'pareto_cdf', 'pareto_lccdf', 'pareto_lcdf', 'pareto_lpdf', 'pareto_rng', 'pareto_type_2_cdf', 'pareto_type_2_lccdf', 'pareto_type_2_lcdf', 'pareto_type_2_lpdf', 'pareto_type_2_rng', 'pi', 'poisson_cdf', 'poisson_lccdf', 'poisson_lcdf', 'poisson_log_lpmf', 'poisson_log_rng', 'poisson_lpmf', 'poisson_rng', 'positive_infinity', 'pow', 'print', 'prod', 'qr_Q', 'qr_R', 'quad_form', 'quad_form_diag', 'quad_form_sym', 'rank', 'rayleigh_cdf', 'rayleigh_lccdf', 'rayleigh_lcdf', 'rayleigh_lpdf', 'rayleigh_rng', 'reject', 'rep_array', 'rep_matrix', 'rep_row_vector', 'rep_vector', 'rising_factorial', 'round', 'row', 'rows', 'rows_dot_product', 'rows_dot_self', 'scaled_inv_chi_square_cdf', 'scaled_inv_chi_square_lccdf', 'scaled_inv_chi_square_lcdf', 'scaled_inv_chi_square_lpdf', 'scaled_inv_chi_square_rng', 'sd', 'segment', 'sin', 'singular_values', 'sinh', 'size', 'skew_normal_cdf', 'skew_normal_lccdf', 'skew_normal_lcdf', 'skew_normal_lpdf', 'skew_normal_rng', 'softmax', 'sort_asc', 'sort_desc', 'sort_indices_asc', 'sort_indices_desc', 'sqrt', 'sqrt2', 'square', 'squared_distance', 'step', 'student_t_cdf', 'student_t_lccdf', 'student_t_lcdf', 'student_t_lpdf', 'student_t_rng', 'sub_col', 'sub_row', 'sum', 'tail', 'tan', 'tanh', 'target', 'tcrossprod', 'tgamma', 'to_array_1d', 'to_array_2d', 'to_matrix', 'to_row_vector', 'to_vector', 'trace', 'trace_gen_quad_form', 'trace_quad_form', 'trigamma', 'trunc', 'uniform_cdf', 'uniform_lccdf', 'uniform_lcdf', 'uniform_lpdf', 'uniform_rng', 'variance', 'von_mises_lpdf', 'von_mises_rng', 'weibull_cdf', 'weibull_lccdf', 'weibull_lcdf', 'weibull_lpdf', 'weibull_rng', 'wiener_lpdf', 'wishart_lpdf', 'wishart_rng'];\n var DISTRIBUTIONS = ['bernoulli', 'bernoulli_logit', 'beta', 'beta_binomial', 'binomial', 'binomial_logit', 'categorical', 'categorical_logit', 'cauchy', 'chi_square', 'dirichlet', 'double_exponential', 'exp_mod_normal', 'exponential', 'frechet', 'gamma', 'gaussian_dlm_obs', 'gumbel', 'hypergeometric', 'inv_chi_square', 'inv_gamma', 'inv_wishart', 'lkj_corr', 'lkj_corr_cholesky', 'logistic', 'lognormal', 'multi_gp', 'multi_gp_cholesky', 'multi_normal', 'multi_normal_cholesky', 'multi_normal_prec', 'multi_student_t', 'multinomial', 'neg_binomial', 'neg_binomial_2', 'neg_binomial_2_log', 'normal', 'ordered_logistic', 'pareto', 'pareto_type_2', 'poisson', 'poisson_log', 'rayleigh', 'scaled_inv_chi_square', 'skew_normal', 'student_t', 'uniform', 'von_mises', 'weibull', 'wiener', 'wishart'];\n return {\n name: 'Stan',\n aliases: ['stanfuncs'],\n keywords: {\n $pattern: hljs.IDENT_RE,\n title: BLOCKS.join(' '),\n keyword: STATEMENTS.concat(VAR_TYPES).concat(SPECIAL_FUNCTIONS).join(' '),\n built_in: FUNCTIONS.join(' ')\n },\n contains: [hljs.C_LINE_COMMENT_MODE, hljs.COMMENT(/#/, /$/, {\n relevance: 0,\n keywords: {\n 'meta-keyword': 'include'\n }\n }), hljs.COMMENT(/\\/\\*/, /\\*\\//, {\n relevance: 0,\n // highlight doc strings mentioned in Stan reference\n contains: [{\n className: 'doctag',\n begin: /@(return|param)/\n }]\n }), {\n // hack: in range constraints, lower must follow \"<\"\n begin: /<\\s*lower\\s*=/,\n keywords: 'lower'\n }, {\n // hack: in range constraints, upper must follow either , or <\n // <lower = ..., upper = ...> or <upper = ...>\n begin: /[<,]\\s*upper\\s*=/,\n keywords: 'upper'\n }, {\n className: 'keyword',\n begin: /\\btarget\\s*\\+=/,\n relevance: 10\n }, {\n begin: '~\\\\s*(' + hljs.IDENT_RE + ')\\\\s*\\\\(',\n keywords: DISTRIBUTIONS.join(' ')\n }, {\n className: 'number',\n variants: [{\n begin: /\\b\\d+(?:\\.\\d*)?(?:[eE][+-]?\\d+)?/\n }, {\n begin: /\\.\\d+(?:[eE][+-]?\\d+)?\\b/\n }],\n relevance: 0\n }, {\n className: 'string',\n begin: '\"',\n end: '\"',\n relevance: 0\n }]\n };\n}\n\nmodule.exports = stan;"],"sourceRoot":""} |