CausalMixGPD
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On this page

  • Function Reference by Job

Function Reference by Job

Explicit, job-based routing into pkgdown function topics

Function Reference by Job

Use this page when you want function-level docs grouped by what each function is used for.

Full alphabetical reference Reference hub Examples hub

Note

The links below point to pkgdown topic pages generated from Rd files. If API topics are added or renamed, update this page and rebuild docs.

Build models and run MCMC

Use these when preparing bundles and running one-arm, causal, or clustering model fits.

  • bundle() workflow bundle
  • build_nimble_bundle() explicit one-arm bundle
  • build_causal_bundle() two-arm causal bundle
  • dpmix() one-arm bulk fit
  • dpmgpd() one-arm bulk plus tail fit
  • dpmix.causal() causal bulk fit
  • dpmgpd.causal() causal bulk plus tail fit
  • dpmix.cluster() clustering bulk fit
  • dpmgpd.cluster() clustering bulk plus tail fit
  • mcmc() run posterior sampling
  • params() extract posterior mean parameters
  • run_mcmc_bundle_manual() low-level one-arm runner
  • run_mcmc_causal() low-level causal runner

Related workflow pages: Start hub, Examples, Backends and workflow

Predict, summarize, print, and plot fitted objects

Use these after fitting to generate predictions, diagnostics, fitted values, and plots.

  • predict()
  • summary()
  • fitted()
  • residuals()
  • ess_summary() effective sample size summary
  • plot()
  • plot()
  • plot()
  • print()
  • print()
  • print()
  • print()
  • print()

Related workflow pages: Start here, Basic compile and run

Estimate causal effects and inspect causal outputs

Use these when extracting treatment effects from fitted causal models.

  • predict()
  • ate() average treatment effect
  • att() treated-standardized average treatment effect
  • cate() conditional average treatment effects
  • qte() quantile treatment effects
  • cqte() conditional quantile treatment effects
  • qtt() quantile treatment effects standardized to treated covariates
  • ate_rmean() restricted-mean ATE helper
  • summary()
  • summary()
  • summary()
  • summary()
  • summary()
  • plot()
  • plot()
  • plot()
  • plot()
  • print()
  • print()
  • print()
  • print()
  • print()
  • print()
  • print(summary())
  • print(summary())
  • print()
  • print()

Related workflow pages: Causal track, Causal examples

Run clustering workflows and diagnostics

Use these when assigning clusters, inspecting posterior cluster summaries, and plotting clustering diagnostics.

  • allocation() cluster label and PSM outputs
  • allocation()
  • predict()
  • summary()
  • summary()
  • summary()
  • summary()
  • summary()
  • print()
  • print()
  • print()
  • print()
  • print()
  • plot()
  • plot()
  • plot()
  • plot()
  • plot()

Related workflow pages: Clustering track, Clustering examples

Choose kernels, tails, registries, and diagnostics

Use these when selecting kernel families, tail behavior, and glue diagnostics.

  • kernel_support_table() available kernel support and domains
  • get_kernel_registry()
  • init_kernel_registry()
  • get_tail_registry()
  • check_glue_validity() bulk-tail glue checks
  • gpd distribution helpers
  • normal kernels and mixtures
  • normal with GPD
  • normal mixture with GPD
  • normal lowercase vectorized wrappers
  • lognormal kernels and mixtures
  • lognormal with GPD
  • lognormal mixture with GPD
  • lognormal lowercase vectorized wrappers
  • gamma kernels and mixtures
  • gamma with GPD
  • gamma mixture with GPD
  • gamma lowercase vectorized wrappers
  • inverse Gaussian base
  • inverse Gaussian with GPD
  • inverse Gaussian mixture
  • inverse Gaussian mixture with GPD
  • inverse Gaussian lowercase vectorized wrappers
  • laplace kernels and mixtures
  • laplace with GPD
  • laplace mixture with GPD
  • laplace lowercase vectorized wrappers
  • cauchy base
  • cauchy mixture
  • cauchy lowercase vectorized wrappers
  • amoroso base
  • amoroso with GPD
  • amoroso mixture
  • amoroso mixture with GPD
  • amoroso lowercase vectorized wrappers
  • base lowercase vectorized wrappers

Scalar d/p/q/r aliases by family

Use these when you want direct scalar/vector-style d(), p(), q(), r() naming for a specific family implementation.

  • Amoroso aliases, Amoroso GPD aliases, Amoroso mixture aliases, Amoroso mixture+GPD aliases
  • Cauchy scalar aliases, Cauchy vector aliases, Cauchy mixture aliases
  • Gamma GPD aliases, Gamma mixture aliases, Gamma mixture+GPD aliases
  • GPD aliases
  • Inverse Gaussian aliases, Inverse Gaussian GPD aliases, Inverse Gaussian mixture aliases, Inverse Gaussian mixture+GPD aliases
  • Laplace GPD aliases, Laplace mixture aliases, Laplace mixture+GPD aliases
  • Lognormal GPD aliases, Lognormal mixture aliases, Lognormal mixture+GPD aliases
  • Normal GPD aliases, Normal mixture aliases, Normal mixture+GPD aliases

Related workflow pages: Kernels hub, Kernels and tails track, Advanced hub

Use packaged datasets and simulation helpers

Use these to bootstrap examples, reproduce docs, and test workflows.

  • causal_pos500_p3_k2
  • causal_alt_pos500_p3_k3
  • causal_alt_pos500_p5_k4_tail
  • causal_alt_real500_p4_k2
  • nc_pos200_k3
  • nc_pos_tail200_k4
  • nc_posX100_p3_k2
  • nc_posX100_p4_k3
  • nc_posX100_p5_k4
  • nc_real200_k2
  • nc_realX100_p3_k2
  • nc_realX100_p5_k3
  • sim_bulk_tail() simulation utility
  • sim_survival_tail() simulation utility
  • sim_causal_qte() simulation utility

Related workflow pages: Examples hub, Start hub

Developer and internal APIs

Use these only when extending internals or debugging package internals and dispatch behavior.

Warning

Internal APIs (dot-prefixed topics) may change without the same stability guarantees as user-facing wrappers.

  • dot-get_dispatch()
  • dot-get_dispatch_scalar()
  • dot-compute_cluster_probs()
  • dot-compute_psm()
  • dot-dahl_representative()
  • dot-detect_first_present()
  • dot-extract_draws()
  • dot-extract_draws_matrix()
  • dot-extract_nimble_code()
  • dot-extract_z_matrix()
  • dot-format_fit_header()
  • dot-get_epsilon()
  • dot-get_nobs()
  • dot-get_samples_mcmclist()
  • dot-onLoad()
  • dot-posterior_summarize()
  • dot-predict_mixgpd()
  • dot-summarize_posterior()
  • dot-truncate_components_one_draw()
  • dot-truncate_draws_matrix_components()
  • dot-truncation_info()
  • dot-wrap_nimble_code()
  • dot-wrap_scalar_first_arg()
  • dot-wrap_scalar_p()
  • dot-wrap_scalar_r()
  • dot-validate_fit()

Package globals and options

  • globals

Related workflow pages: Developers hub, Spec and contracts, Registry docs

Prefer a full scan?

Use the complete index when you want all topics in one place.

Open full pkgdown reference index

(c) CausalMixGPD - Bayesian semiparametric modeling for heavy-tailed data
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