vue-hello/node_modules/highlight.js/lib/languages/stan.js

/*
Language: Stan
Description: The Stan probabilistic programming language
Author: Jeffrey B. Arnold <jeffrey.arnold@gmail.com>
Website: http://mc-stan.org/
Category: scientific
*/

function stan(hljs) {
  // variable names cannot conflict with block identifiers
  const BLOCKS = [
    'functions',
    'model',
    'data',
    'parameters',
    'quantities',
    'transformed',
    'generated'
  ];
  const STATEMENTS = [
    'for',
    'in',
    'if',
    'else',
    'while',
    'break',
    'continue',
    'return'
  ];
  const SPECIAL_FUNCTIONS = [
    'print',
    'reject',
    'increment_log_prob|10',
    'integrate_ode|10',
    'integrate_ode_rk45|10',
    'integrate_ode_bdf|10',
    'algebra_solver'
  ];
  const 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'
  ];
  const 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'
  ];
  const 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'
  ];

  return {
    name: 'Stan',
    aliases: [ 'stanfuncs' ],
    keywords: {
      $pattern: hljs.IDENT_RE,
      title: BLOCKS,
      keyword: STATEMENTS.concat(VAR_TYPES).concat(SPECIAL_FUNCTIONS),
      built_in: FUNCTIONS
    },
    contains: [
      hljs.C_LINE_COMMENT_MODE,
      hljs.COMMENT(
        /#/,
        /$/,
        {
          relevance: 0,
          keywords: {
            'meta-keyword': 'include'
          }
        }
      ),
      hljs.COMMENT(
        /\/\*/,
        /\*\//,
        {
          relevance: 0,
          // highlight doc strings mentioned in Stan reference
          contains: [
            {
              className: 'doctag',
              begin: /@(return|param)/
            }
          ]
        }
      ),
      {
        // hack: in range constraints, lower must follow "<"
        begin: /<\s*lower\s*=/,
        keywords: 'lower'
      },
      {
        // hack: in range constraints, upper must follow either , or <
        // <lower = ..., upper = ...> or <upper = ...>
        begin: /[<,]\s*upper\s*=/,
        keywords: 'upper'
      },
      {
        className: 'keyword',
        begin: /\btarget\s*\+=/,
        relevance: 10
      },
      {
        begin: '~\\s*(' + hljs.IDENT_RE + ')\\s*\\(',
        keywords: DISTRIBUTIONS
      },
      {
        className: 'number',
        variants: [
          {
            begin: /\b\d+(?:\.\d*)?(?:[eE][+-]?\d+)?/
          },
          {
            begin: /\.\d+(?:[eE][+-]?\d+)?\b/
          }
        ],
        relevance: 0
      },
      {
        className: 'string',
        begin: '"',
        end: '"',
        relevance: 0
      }
    ]
  };
}

module.exports = stan;