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/*
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;