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