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Update Stan syntax to version 2.33 (#3859)
* Update Stan syntax to version 2.33 * Update CHANGES.md
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‎CHANGES.md‎

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@@ -40,6 +40,7 @@ Core Grammars:
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- fix(reasonml) simplify syntax and align it with ocaml [jchavarri][]
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- fix(swift) `warn_unqualified_access` is an attribute [Bradley Mackey][]
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- enh(swift) macro attributes are highlighted as keywords [Bradley Mackey][]
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- enh(stan) updated for version 2.33 (#3859) [Brian Ward][]
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Dev tool:
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@@ -62,6 +63,7 @@ Dev tool:
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[aana-h2]: https://github.com/aana-h2
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[Nicholas Thompson]: https://github.com/NAThompson
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[Yasith Deelaka]: https://github.com/YasithD
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[Brian Ward]: https://github.com/WardBrian
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## Version 11.8.0

‎src/languages/stan.js‎

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@@ -32,16 +32,20 @@ export default function(hljs) {
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const TYPES = [
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'array',
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'tuple',
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'complex',
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'int',
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'real',
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'vector',
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'complex_vector',
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'ordered',
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'positive_ordered',
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'simplex',
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'unit_vector',
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'row_vector',
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'complex_row_vector',
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'matrix',
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'complex_matrix',
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'cholesky_factor_corr|10',
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'cholesky_factor_cov|10',
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'corr_matrix|10',
@@ -58,8 +62,6 @@ export default function(hljs) {
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// functions_quoted.txt
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const FUNCTIONS = [
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'Phi',
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'Phi_approx',
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'abs',
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'acos',
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'acosh',
@@ -77,7 +79,6 @@ export default function(hljs) {
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'bessel_first_kind',
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'bessel_second_kind',
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'binary_log_loss',
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'binomial_coefficient_log',
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'block',
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'cbrt',
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'ceil',
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'cols',
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'columns_dot_product',
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'columns_dot_self',
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'complex_schur_decompose',
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'complex_schur_decompose_t',
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'complex_schur_decompose_u',
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'conj',
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'cos',
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'cosh',
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'cov_exp_quad',
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'crossprod',
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'csr_extract',
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'csr_extract_u',
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'csr_extract_v',
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'csr_extract_w',
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'csr_matrix_times_vector',
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'csr_to_dense_matrix',
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'cumulative_sum',
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'dae',
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'dae_tol',
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'determinant',
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'diag_matrix',
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'diagonal',
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'diag_post_multiply',
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'diag_pre_multiply',
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'diagonal',
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'digamma',
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'dims',
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'distance',
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'dot_product',
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'dot_self',
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'eigendecompose',
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'eigendecompose_sym',
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'eigenvalues',
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'eigenvalues_sym',
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'eigenvectors',
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'eigenvectors_sym',
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'erf',
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'erfc',
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'exp',
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'exp2',
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'expm1',
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'fabs',
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'falling_factorial',
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'fdim',
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'fft',
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'fft2',
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'floor',
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'fma',
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'fmax',
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'gamma_q',
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'generalized_inverse',
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'get_imag',
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'get_lp',
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'get_real',
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'head',
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'hmm_hidden_state_prob',
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'hmm_marginal',
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'hypot',
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'identity_matrix',
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'inc_beta',
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'int_step',
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'integrate_1d',
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'integrate_ode',
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'integrate_ode_adams',
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'integrate_ode_bdf',
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'integrate_ode_rk45',
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'int_step',
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'inv',
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'inv_Phi',
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'inv_cloglog',
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'inv_erfc',
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'inverse',
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'inverse_spd',
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'inv_fft',
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'inv_fft2',
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'inv_inc_beta',
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'inv_logit',
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'inv_Phi',
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'inv_sqrt',
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'inv_square',
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'inverse',
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'inverse_spd',
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'is_inf',
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'is_nan',
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'lambert_w0',
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'log_falling_factorial',
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'log_inv_logit',
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'log_inv_logit_diff',
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'logit',
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'log_mix',
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'log_modified_bessel_first_kind',
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'log_rising_factorial',
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'log_softmax',
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'log_sum_exp',
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'logit',
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'machine_precision',
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'map_rect',
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'matrix_exp',
@@ -195,10 +210,11 @@ export default function(hljs) {
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'min',
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'modified_bessel_first_kind',
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'modified_bessel_second_kind',
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'multiply_log',
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'multiply_lower_tri_self_transpose',
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'negative_infinity',
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'norm',
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'norm1',
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'norm2',
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'not_a_number',
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'num_elements',
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'ode_adams',
@@ -219,14 +235,18 @@ export default function(hljs) {
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'ones_row_vector',
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'ones_vector',
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'owens_t',
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'Phi',
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'Phi_approx',
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'polar',
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'positive_infinity',
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'pow',
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'print',
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'prod',
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'proj',
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'qr',
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'qr_Q',
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'qr_R',
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'qr_thin',
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'qr_thin_Q',
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'qr_thin_R',
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'quad_form',
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'sub_col',
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'sub_row',
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'sum',
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'svd',
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'svd_U',
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'svd_V',
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'symmetrize_from_lower_tri',
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'to_array_1d',
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'to_array_2d',
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'to_complex',
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'to_int',
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'to_matrix',
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'to_row_vector',
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'to_vector',
@@ -321,18 +343,22 @@ export default function(hljs) {
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'inv_chi_square',
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'inv_gamma',
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'inv_wishart',
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'inv_wishart_cholesky',
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'lkj_corr',
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'lkj_corr_cholesky',
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'logistic',
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'loglogistic',
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'lognormal',
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'multi_gp',
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'multi_gp_cholesky',
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'multinomial',
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'multinomial_logit',
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'multi_normal',
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'multi_normal_cholesky',
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'multi_normal_prec',
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'multi_student_cholesky_t',
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'multi_student_t',
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'multinomial',
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'multinomial_logit',
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'multi_student_t_cholesky',
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'neg_binomial',
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'neg_binomial_2',
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'neg_binomial_2_log',
@@ -352,12 +378,14 @@ export default function(hljs) {
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'skew_double_exponential',
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'skew_normal',
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'std_normal',
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'std_normal_log',
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'student_t',
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'uniform',
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'von_mises',
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'weibull',
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'wiener',
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'wishart'
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'wishart',
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'wishart_cholesky'
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];
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const BLOCK_COMMENT = hljs.COMMENT(

‎test/detect/stan/default.txt‎

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@@ -5,6 +5,7 @@ data {
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int<lower=0> N;
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int K;
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matrix[N, K] x;
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complex_row_vector[K] z;
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}
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transformed data {
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complex zi = 1+3.14i;
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array[K - 1] real mu;
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array[K + 1] real<lower=0> sigma;
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cholesky_factor_corr[K] L;
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tuple(real, tuple(real, real)) t;
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}
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model {
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target += normal_lpdf(x[ : , 1] | mu[1], sigma[1]);
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target += gumbel_lpdf(x[ : , 2] | mu[2], sigma[2]);
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target += lognormal_lpdf(x[ : , 3] | mu[3], sigma[3]);
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target += weibull_lpdf(x[ : , 4] | sigma[4], sigma[5]);
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{
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matrix[K, N] y;
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for (n in 1 : N) {
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}
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generated quantities {
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matrix[K, K] Sigma = multiply_lower_tri_self_transpose(L);
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}
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}

‎test/markup/stan/default.expect.txt‎

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<span class="hljs-type">int</span>&lt;<span class="hljs-keyword">lower</span>=<span class="hljs-number">0</span>&gt; N;
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<span class="hljs-type">int</span> K;
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<span class="hljs-type">matrix</span>[N, K] x;
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<span class="hljs-type">complex_row_vector</span>[K] z;
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}
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<span class="hljs-title">transformed</span> <span class="hljs-title">data</span> {
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<span class="hljs-type">complex</span> zi = <span class="hljs-number">1</span>+<span class="hljs-number">3.14i</span>;
@@ -16,13 +17,14 @@
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<span class="hljs-type">array</span>[K - <span class="hljs-number">1</span>] <span class="hljs-type">real</span> mu;
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<span class="hljs-type">array</span>[K + <span class="hljs-number">1</span>] <span class="hljs-type">real</span>&lt;<span class="hljs-keyword">lower</span>=<span class="hljs-number">0</span>&gt; sigma;
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<span class="hljs-type">cholesky_factor_corr</span>[K] L;
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<span class="hljs-type">tuple</span>(<span class="hljs-type">real</span>, <span class="hljs-type">tuple</span>(<span class="hljs-type">real</span>, <span class="hljs-type">real</span>)) t;
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}
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<span class="hljs-title">model</span> {
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<span class="hljs-keyword">target</span> += <span class="hljs-built_in">normal_lpdf</span>(x[ : , <span class="hljs-number">1</span>] | mu[<span class="hljs-number">1</span>], sigma[<span class="hljs-number">1</span>]);
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<span class="hljs-keyword">target</span> += <span class="hljs-built_in">gumbel_lpdf</span>(x[ : , <span class="hljs-number">2</span>] | mu[<span class="hljs-number">2</span>], sigma[<span class="hljs-number">2</span>]);
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<span class="hljs-keyword">target</span> += <span class="hljs-built_in">lognormal_lpdf</span>(x[ : , <span class="hljs-number">3</span>] | mu[<span class="hljs-number">3</span>], sigma[<span class="hljs-number">3</span>]);
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<span class="hljs-keyword">target</span> += <span class="hljs-built_in">weibull_lpdf</span>(x[ : , <span class="hljs-number">4</span>] | sigma[<span class="hljs-number">4</span>], sigma[<span class="hljs-number">5</span>]);
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{
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<span class="hljs-type">matrix</span>[K, N] y;
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<span class="hljs-keyword">for</span> (n <span class="hljs-keyword">in</span> <span class="hljs-number">1</span> : N) {
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}
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<span class="hljs-title">generated</span> <span class="hljs-title">quantities</span> {
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<span class="hljs-type">matrix</span>[K, K] Sigma = <span class="hljs-built_in">multiply_lower_tri_self_transpose</span>(L);
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}
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}

‎test/markup/stan/default.txt‎

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int<lower=0> N;
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int K;
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matrix[N, K] x;
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complex_row_vector[K] z;
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}
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transformed data {
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complex zi = 1+3.14i;
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array[K - 1] real mu;
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array[K + 1] real<lower=0> sigma;
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cholesky_factor_corr[K] L;
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tuple(real, tuple(real, real)) t;
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}
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model {
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target += normal_lpdf(x[ : , 1] | mu[1], sigma[1]);
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target += gumbel_lpdf(x[ : , 2] | mu[2], sigma[2]);
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target += lognormal_lpdf(x[ : , 3] | mu[3], sigma[3]);
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target += weibull_lpdf(x[ : , 4] | sigma[4], sigma[5]);
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{
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matrix[K, N] y;
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for (n in 1 : N) {
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3739
}
3840
generated quantities {
3941
matrix[K, K] Sigma = multiply_lower_tri_self_transpose(L);
40-
}
42+
}

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