Man pages for EandrewJones/mmBPFA
Multi-Modal Beta-Process Factor Analysis
| calculate_d_mask | calculates the D subset (rank orderings mask), and number of... |
| calculate_log_lik | calculate log-likelihood by integrating over the posterior... |
| chain_call | Meta-function that bundles the sampler into a chain call |
| check_accuracy | Function to check reconstruction accuracy |
| check_k_start | Selects appropriate K_start based on rank of data |
| check_logliks | Function to examine posterior log-Likelihoods |
| destructure.mcmc.output.processed | zeallot destructure function |
| destructure.mmBPFA.simulated.data | zeallot destructure function |
| extract_omega_lambda_mean_se | function to estimate factor means and se's |
| generate_sim_data | Generate data for running simulations with mmBPFA |
| get_dims_2d | function to get extract dimensions of 2d objects |
| Hpb | Indian Buffet point process parameter function |
| ibp_lbeta | Indian Buffet Process log-beta draws |
| initialize_sampler | generate initial values for single chain |
| mmBPFA-package | Multi-Modal Beta-Process Factor Analysis |
| mmBPFA_sampler | mmBPFA Gibbs sampler function |
| process_benchmarks | Function to process benchmarks |
| process_results | Process results from mmBPFA sampling run |
| remove_inactive | Remove inactive features |
| remove_weakly_active | remove weakly active dimensions |
| sample_alpha | Sampling step for alpha (item-intercepts) |
| sample_d | Sampling step for d (hyperparameter for sample_gamma_k) |
| sample_gamma_k | Sampling step for gamma_k (factor precisions) |
| sample_IBP_a | Sampling step for Indian Buffet Process Alpha Parameter |
| sample_IBP_b | Metropolis-Hastings sampling step for Indian Buffet Process... |
| sample_lambda_and_zeros | Sampling step for lambda and binary, sparsity-inducing zero... |
| sample_omega | Sampling step for omega matrix (Factor Scores) of dimension N... |
| sampler_phase | All-in-one function for the sampling procedure |
| sample_x | sample X values according to Hoff (2007) and Murray et al.... |
| vectorized_which.max | vectorized which.max function for D mask |
