### APIDOC: Documentation Updates for Operators and Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds documentation for infix operators, the categorical_logit sampling statement, and an example for Cholesky factor with unit diagonal transform, improving clarity and usage guidance. ```APIDOC add infix operators to manual categorical_logit sampling statement Cholesky factor with unit diagonal transform example ``` -------------------------------- ### APIDOC: Multivariate Priors for quad_form_diag Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Provides new advice on multivariate priors specifically for the quad_form_diag function, guiding users on best practices for model specification. ```APIDOC new advice on multivariate priors for quad_form_diag ``` -------------------------------- ### APIDOC: CmdStan Integration for Binaries Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Moves bin/print and bin/stanc to CmdStan, indicating a shift in how main executables are generated when compiling models from Stan C++. ```APIDOC move bin/print and bin/stanc to CmdStan; no longer generating main when compiling model from Stan C++ ``` -------------------------------- ### APIDOC: Gaussian Process Densities Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces new densities for Gaussian processes: multi_gp_log() and multi_gp(), enabling more complex statistical modeling. ```APIDOC densities: Gaussian processes multi_gp_log(), multi_gp() ``` -------------------------------- ### APIDOC: a^b Syntax for pow(a,b) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds a^b as a syntactic sugar for pow(a,b), providing a more natural and concise way to express exponentiation. ```APIDOC a^b syntax for pow(a,b) ``` -------------------------------- ### Stan CmdStan: Fixed Parameter Sampler Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt A new `fixed_param` sampler was added for use specifically with the generated quantities block, allowing for fixed parameter sampling without MCMC iterations. ```APIDOC sampler: fixed_param usage: For generated quantities block ``` -------------------------------- ### C++ chainable_alloc Class (v1.3.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces a new C++ class, `chainable_alloc`, designed for caching solver results, improving performance and memory management within the Stan C++ backend. ```APIDOC C++ Class: class chainable_alloc { // Purpose: Caches solver results for improved performance. // Details: Designed for internal use within Stan's C++ backend. } ``` -------------------------------- ### APIDOC: New Matrix Operations Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds new matrix operations including quad_form_sym() (x' *Sigma * x) and QR decompositions (qr_Q(), qr_R()), expanding the linear algebra capabilities. ```APIDOC matrix operations: quad_form_sym() (x' *Sigma * x), QR decompositions qr_Q(), qr_R() ``` -------------------------------- ### APIDOC: Effective Sample Size Documentation Clarification Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Fixes a typo and clarifies the documentation for effective sample size, improving the understanding of diagnostic metrics. ```APIDOC fixed typo and clarified effective sample size doc ``` -------------------------------- ### APIDOC: Zero-Length Inputs for Multivariate Distributions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Allows zero-length inputs for multi_normal and multi_student_t distributions, with an appropriate log probability of 0, enhancing flexibility for edge cases. ```APIDOC allow zero-length inputs to multi_normal and multi_student_t with appropriate log prob (i.e., 0) ``` -------------------------------- ### Stan Broadcast/Repetition Functions (v1.3.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces functions for creating repeated arrays, matrices, and vectors, simplifying initialization and data manipulation in the Stan modeling language. ```APIDOC Functions: rep_array(value, int dim1, ...): Create an array with repeated value rep_matrix(value, int rows, int cols): Create a matrix with repeated value rep_row_vector(value, int cols): Create a row vector with repeated value rep_vector(value, int rows): Create a column vector with repeated value ``` -------------------------------- ### Stan Language: Sort and Rank Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt New `sort` and `rank` functions were added to the Stan language, providing capabilities for ordering and ranking data within models. ```APIDOC sort_asc(array) sort_desc(array) rank(array) ``` -------------------------------- ### APIDOC: New Function Signature Tests Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds tests for log_softmax, multiply_lower_tri_self_transpose, and tcrossprod, ensuring the correctness of these new or updated function signatures. ```APIDOC added tests for log_softmax, multiply_lower_tri_self_transpose, tcrossprod ``` -------------------------------- ### APIDOC: pow Syntax for Vector Usage Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Fixes the pow (^) syntax to correctly catch and disallow illegal use with vectors, ensuring proper type handling. ```APIDOC fix for pow as ^ syntax to catch illegal use of vectors (which aren't supported) ``` -------------------------------- ### APIDOC: Higher-Order Autodiff Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Completes the implementation of higher-order automatic differentiation, extending support to all probability functions, matrix functions, and matrix operators. Tested up to 3rd order derivatives. ```APIDOC completed higher-order autodiff (added all probability functions, matrix functions, and matrix operators); tested up to 3rd order ``` -------------------------------- ### RStan Exposed Log Probability and Gradient Functions (v1.2.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Exposes the model's log probability and gradient functions in RStan, allowing their direct use by external R packages, such as optimizers, for advanced analysis. ```APIDOC RStan Exposed Functions: log_prob(stan_model_object, parameters): Returns the log probability of the model given parameters. gradient(stan_model_object, parameters): Returns the gradient of the log probability with respect to parameters. // Usage: Intended for integration with external R optimization packages. ``` -------------------------------- ### APIDOC: Scalar Operations in Element-Wise and Matrix Division Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Allows the use of scalars in all element-wise operations and enables matrix division for mixing scalars and matrices, increasing flexibility in mathematical expressions. ```APIDOC added ability to use scalars in all element-wise operations allow matrix division for mixing scalars and matrices ``` -------------------------------- ### APIDOC: main() Removal from Generated Models Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Removes main() from models generated from C++ Stan, making them available only in CmdStan and streamlining the compilation process. ```APIDOC removed main() from models generated from C++ Stan (they are now available only in CmdStan); removed no_main command options ``` -------------------------------- ### Stan Language: Negative Binomial Parameterization Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduced an alternative parameterization for the negative binomial distribution, `neg_binomial_2()`, to provide more flexible modeling options within the Stan language. ```APIDOC neg_binomial_2(y | mu, phi) ``` -------------------------------- ### New Stan Probability Distributions (v1.3.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces new probability distributions available in the Stan modeling language, expanding the range of statistical models that can be directly specified. ```APIDOC Distributions: exp_mod_normal gumbel skew_normal ``` -------------------------------- ### RStan as.mcmc.list() Method (v1.3.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds a method to convert Stan output objects into an 'mcmc.list' object, facilitating compatibility with other R packages for MCMC analysis. ```APIDOC RStan Method: as.mcmc.list(stan_fit_object): Converts a Stan fit object to an mcmc.list object. ``` -------------------------------- ### Stan Language: New Mathematical Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Added `distance`, `squared_distance`, and `diag_pre_multiply` to the Stan modeling language for common mathematical operations, enhancing model expressiveness. ```APIDOC distance(vector1, vector2) squared_distance(vector1, vector2) diag_pre_multiply(vector, matrix) ``` -------------------------------- ### APIDOC: 1-Indexed Error Messages Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Modifies error messages to be 1-indexed instead of 0-indexed, providing a more user-friendly and consistent experience. ```APIDOC modified error messages so that they're all 1-indexed instead of 0-indexed ``` -------------------------------- ### APIDOC: User-Defined Function Bug Fixes Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Addresses several issues related to user-defined functions, including preventing double compilation, fixing problems with int-only parameters, resolving NaN initialization, and adding checks for variable name conflicts. ```APIDOC fixed problem with compiling user-defined function twice fixed problem with int-only parameters for user-defined functions fixed NaN init problems for user-defined functions added check that user variable doesn't conflict with user function + doc disallow void argument types in user-defined functions ``` -------------------------------- ### APIDOC: Multivariate Normal Vectorization and Mean Generalization Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces vectorization of outcome variates in multivariate normal distributions with efficiency boosts and generalizes the function to allow row vectors as means. ```APIDOC vectorization of outcome variates in multivariate normal with efficiency boosts generalization of multivariate normal to allow rwo vectors as means ``` -------------------------------- ### APIDOC: User-Defined Function Definitions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces the ability to define user-defined functions directly within the Stan language, significantly enhancing model expressiveness and reusability. ```APIDOC user-defined function definitions added to Stan language ``` -------------------------------- ### APIDOC: std::cout Removal from API Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Initiates the removal of std::cout instances from the Stan API, aiming for cleaner output and better control over logging. ```APIDOC removing many instances of std::cout from API (more still to go) ``` -------------------------------- ### APIDOC: L-BFGS Optimization Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces L-BFGS as the new default optimization algorithm in Stan. This enhancement improves the efficiency and performance of model fitting. ```APIDOC L-BFGS optimization (now the default) ``` -------------------------------- ### Stan Language: Pochhammer Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Pochhammer functions, representing rising and falling factorials, were added to the Stan language for combinatorial and special function calculations. ```APIDOC pochhammer_rising(x, n) pochhammer_falling(x, n) ``` -------------------------------- ### Stan Language: Rayleigh Distribution Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt The Rayleigh distribution was added to the Stan language, expanding the range of available probability distributions for modeling. ```APIDOC rayleigh_lpdf(y | sigma) rayleigh_lcdf(y | sigma) rayleigh_lccdf(y | sigma) rayleigh_rng(sigma) ``` -------------------------------- ### APIDOC: multiply_lower_self_transpose Typo Fix Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Corrects a typo in the documentation for multiply_lower_self_transpose, ensuring accuracy in function references. ```APIDOC fix typo in multiply_lower_self_transpose (thanks to Alexey Stukalov) ``` -------------------------------- ### APIDOC: von Mises Random Number Generator Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds a new random number generator for the von Mises distribution, expanding the set of available statistical functions. ```APIDOC added von Mises RNG ``` -------------------------------- ### APIDOC: normal_cdf Range Enhancement Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Enhances the effective range of the normal_cdf function to prevent numerical underflow and overflow issues, improving its robustness. ```APIDOC enhanced effective range of normal_cdf to prevent underflow/overflow ``` -------------------------------- ### Stan Language: Sorting Indices Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt New `sort_indices_*()` functions were added to return indices in sorted order based on value, useful for custom sorting logic and data manipulation within Stan models. ```APIDOC sort_indices_asc(array) sort_indices_desc(array) ``` -------------------------------- ### APIDOC: Reshaping Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Introduces new functions for reshaping data structures: to_matrix(), to_vector(), to_row_vector(), to_array_1d(), and to_array_2d(), facilitating data manipulation. ```APIDOC reshaping functions: to_matrix(), to_vector(), to_row_vector(), to_array_1d(), to_array_2d() ``` -------------------------------- ### Stan Language: Increment Log Probability Function Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt The `increment_log_prob()` function was introduced as the preferred method for incrementing the log probability, deprecating direct use of `lp__`. It also gained vectorized support for efficiency. ```APIDOC increment_log_prob(value) // Deprecates direct use of lp__ ``` -------------------------------- ### Stan Language: Bessel Functions Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Bessel functions of the first and second kind, both standard and modified, were added to the Stan language for advanced mathematical modeling. ```APIDOC bessel_first_kind(nu, z) bessel_second_kind(nu, z) modified_bessel_first_kind(nu, z) modified_bessel_second_kind(nu, z) ``` -------------------------------- ### Stan Language: Multi-Normal Cholesky Random Number Generator Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Added `multi_normal_cholesky_rng()` for generating random numbers from a multi-normal distribution parameterized by its Cholesky factor, enhancing sampling capabilities. ```APIDOC multi_normal_cholesky_rng(mu, L) ``` -------------------------------- ### Stan Language: Cholesky Factor Data Type Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt A new `cholesky_factor` data type was introduced to the Stan language, allowing for more efficient and robust handling of Cholesky factors in models. ```APIDOC cholesky_factor_cov[N] ``` -------------------------------- ### New Stan Mathematical Functions (v1.3.0) Source: https://github.com/stan-dev/stan/blob/develop/RELEASE-NOTES.txt Adds several new mathematical functions to the Stan language, including specialized matrix operations and statistical functions like Owen's T function. ```APIDOC Functions: mdivide_right_spd(matrix A, matrix B): Matrix division A * B^-1 for symmetric positive definite B mdivide_left_spd(matrix A, matrix B): Matrix division A^-1 * B for symmetric positive definite A owenst(real h, real a): Owen's T function columns_dot_product(matrix A, matrix B): Dot product of corresponding columns rows_dot_product(matrix A, matrix B): Dot product of corresponding rows ``` === COMPLETE CONTENT === This response contains all available snippets from this library. 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