A B C D E F G H I L M N P Q R S T U W misc
| multinma-package | multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stan |
| adapt_delta | Target average acceptance probability |
| add_integration | Add numerical integration points to aggregate data |
| add_integration.data.frame | Add numerical integration points to aggregate data |
| add_integration.default | Add numerical integration points to aggregate data |
| add_integration.nma_data | Add numerical integration points to aggregate data |
| as.array.nma_rank_probs | Methods for 'nma_summary' objects |
| as.array.nma_summary | Methods for 'nma_summary' objects |
| as.array.stan_nma | Convert samples into arrays, matrices, or data frames |
| as.data.frame.nma_summary | Methods for 'nma_summary' objects |
| as.data.frame.nodesplit_summary | Methods for 'nodesplit_summary' objects |
| as.data.frame.stan_nma | Convert samples into arrays, matrices, or data frames |
| as.igraph.nma_data | Convert networks to graph objects |
| as.matrix.nma_rank_probs | Methods for 'nma_summary' objects |
| as.matrix.nma_summary | Methods for 'nma_summary' objects |
| as.matrix.stan_nma | Convert samples into arrays, matrices, or data frames |
| as.stanfit | as.stanfit |
| as.stanfit.default | as.stanfit |
| as.stanfit.stan_nma | as.stanfit |
| as.tibble.nma_summary | Methods for 'nma_summary' objects |
| as.tibble.nodesplit_summary | Methods for 'nodesplit_summary' objects |
| as_tbl_graph.nma_data | Convert networks to graph objects |
| as_tibble.nma_summary | Methods for 'nma_summary' objects |
| as_tibble.nodesplit_summary | Methods for 'nodesplit_summary' objects |
| atrial_fibrillation | Stroke prevention in atrial fibrillation patients |
| bcg_vaccine | BCG vaccination |
| blocker | Beta blockers to prevent mortality after MI |
| cauchy | Prior distributions |
| combine_network | Combine multiple data sources into one network |
| dbern | The Bernoulli Distribution |
| dgamma | The Gamma distribution |
| dgent | Generalised Student's t distribution (with location and scale) |
| diabetes | Incidence of diabetes in trials of antihypertensive drugs |
| dic | Deviance Information Criterion (DIC) |
| dietary_fat | Reduced dietary fat to prevent mortality |
| distr | Specify a general marginal distribution |
| dlogitnorm | The logit Normal distribution |
| example_pso_mlnmr | Example plaque psoriasis ML-NMR |
| example_smk_fe | Example smoking FE NMA |
| example_smk_nodesplit | Example smoking node-splitting |
| example_smk_re | Example smoking RE NMA |
| example_smk_ume | Example smoking UME NMA |
| exponential | Prior distributions |
| flat | Prior distributions |
| get_nodesplits | Direct and indirect evidence |
| half_cauchy | Prior distributions |
| half_normal | Prior distributions |
| half_student_t | Prior distributions |
| has_direct | Direct and indirect evidence |
| has_indirect | Direct and indirect evidence |
| hta_psoriasis | HTA Plaque Psoriasis |
| is_network_connected | Check network connectedness |
| log_normal | Prior distributions |
| loo | Model comparison using the 'loo' package |
| loo.stan_nma | Model comparison using the 'loo' package |
| mcmc_array | Working with 3D MCMC arrays |
| mcmc_array-class | Working with 3D MCMC arrays |
| mlnmr_data | The nma_data class |
| mlnmr_data-class | The nma_data class |
| multi | Multinomial outcome data |
| multinma | multinma: A Package for Network Meta-Analysis of Individual and Aggregate Data in Stan |
| names.mcmc_array | Working with 3D MCMC arrays |
| names<-.mcmc_array | Working with 3D MCMC arrays |
| nma | Network meta-analysis models |
| nma_data | The nma_data class |
| nma_data-class | The nma_data class |
| nma_dic | The nma_dic class |
| nma_dic-class | The nma_dic class |
| nma_nodesplit | The nma_nodesplit class |
| nma_nodesplit-class | The nma_nodesplit class |
| nma_nodesplit_df | The nma_nodesplit class |
| nma_nodesplit_df-class | The nma_nodesplit class |
| nma_prior | The nma_prior class |
| nma_prior-class | The nma_prior class |
| nma_rank_probs | The 'nma_summary' class |
| nma_summary | The 'nma_summary' class |
| nma_summary-class | The 'nma_summary' class |
| nodesplit_summary | The 'nodesplit_summary' class |
| nodesplit_summary-class | The 'nodesplit_summary' class |
| normal | Prior distributions |
| pairs.stan_nma | Matrix of plots for a 'stan_nma' object |
| parkinsons | Mean off-time reduction in Parkison's disease |
| pbern | The Bernoulli Distribution |
| pgamma | The Gamma distribution |
| pgent | Generalised Student's t distribution (with location and scale) |
| plaque_psoriasis | Plaque psoriasis data |
| plaque_psoriasis_agd | Plaque psoriasis data |
| plaque_psoriasis_ipd | Plaque psoriasis data |
| plogitnorm | The logit Normal distribution |
| plot.nma_data | Network plots |
| plot.nma_dic | Plots of model fit diagnostics |
| plot.nma_nodesplit | Summarise the results of node-splitting models |
| plot.nma_nodesplit_df | Summarise the results of node-splitting models |
| plot.nma_parameter_summary | Plots of summary results |
| plot.nma_rank_probs | Plots of summary results |
| plot.nma_summary | Plots of summary results |
| plot.nodesplit_summary | Plots of node-splitting models |
| plot.stan_nma | Posterior summaries from 'stan_nma' objects |
| plot_integration_error | Plot numerical integration error |
| plot_prior_posterior | Plot prior vs posterior distribution |
| posterior_ranks | Treatment rankings and rank probabilities |
| posterior_rank_probs | Treatment rankings and rank probabilities |
| predict.stan_nma | Predictions of absolute effects from NMA models |
| print.mcmc_array | Working with 3D MCMC arrays |
| print.mlnmr_data | Print 'nma_data' objects |
| print.nma_data | Print 'nma_data' objects |
| print.nma_dic | Print DIC details |
| print.nma_nodesplit | Print 'nma_nodesplit_df' objects |
| print.nma_nodesplit_df | Print 'nma_nodesplit_df' objects |
| print.nma_summary | Methods for 'nma_summary' objects |
| print.nodesplit_summary | Methods for 'nodesplit_summary' objects |
| print.stan_nma | Print 'stan_nma' objects |
| priors | Prior distributions |
| qbern | The Bernoulli Distribution |
| qgamma | The Gamma distribution |
| qgent | Generalised Student's t distribution (with location and scale) |
| qlogitnorm | The logit Normal distribution |
| relative_effects | Relative treatment effects |
| RE_cor | Random effects structure |
| set_agd_arm | Set up arm-based aggregate data |
| set_agd_contrast | Set up contrast-based aggregate data |
| set_ipd | Set up individual patient data |
| smoking | Smoking cessation data |
| stan_mlnmr | The stan_nma class |
| stan_nma | The stan_nma class |
| stan_nma-class | The stan_nma class |
| statins | Statins for cholesterol lowering |
| student_t | Prior distributions |
| summary.mcmc_array | Working with 3D MCMC arrays |
| summary.nma_nodesplit | Summarise the results of node-splitting models |
| summary.nma_nodesplit_df | Summarise the results of node-splitting models |
| summary.nma_prior | Summary of prior distributions |
| summary.stan_nma | Posterior summaries from 'stan_nma' objects |
| theme_multinma | Plot theme for multinma plots |
| thrombolytics | Thrombolytic treatments data |
| transfusion | Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction |
| unnest_integration | Add numerical integration points to aggregate data |
| waic | Model comparison using the 'loo' package |
| waic.stan_nma | Model comparison using the 'loo' package |
| which_RE | Random effects structure |
| .default | Set default values |
| .is_default | Set default values |