Package index • CausalMixGPD

All functions

dAmoroso() pAmoroso() qAmoroso() rAmoroso(): Amoroso distribution

dAmorosoGpd() pAmorosoGpd() rAmorosoGpd() qAmorosoGpd(): Amoroso with a GPD tail

damorosomix() pamorosomix() qamorosomix() ramorosomix() damorosomixgpd() pamorosomixgpd() qamorosomixgpd() ramorosomixgpd() damorosogpd() pamorosogpd() qamorosogpd() ramorosogpd(): Lowercase vectorized Amoroso distribution functions

dAmorosoMix() pAmorosoMix() rAmorosoMix() qAmorosoMix(): Amoroso mixture distribution

dAmorosoMixGpd() pAmorosoMixGpd() rAmorosoMixGpd() qAmorosoMixGpd(): Amoroso mixture with a GPD tail

ate(): Average treatment effects, marginal over the empirical covariate distribution

ate_rmean(): Restricted-mean ATE helper

att(): Average treatment effects standardized to treated covariates

dgpd() pgpd() qgpd() rgpd() dinvgauss() pinvgauss() qinvgauss() rinvgauss() damoroso() pamoroso() qamoroso() ramoroso() dcauchy_vec() pcauchy_vec() qcauchy_vec() rcauchy_vec(): Lowercase vectorized distribution functions (base kernels)

build_causal_bundle(): Build a causal bundle (design + two outcome arms)

build_code_from_spec(): Build NIMBLE model code from a compiled model spec

build_constants_from_spec(): Build constants list from a compiled model spec

build_dimensions_from_spec(): Build dimension declarations from a compiled model spec

build_inits_from_spec(): Build initial values from a compiled model spec

build_monitors_from_spec(): Build default monitors from a compiled model spec

build_nimble_bundle(): Build the explicit one-arm NIMBLE bundle

bundle(): Build the workflow bundle used by the package fitters

cate(): Conditional average treatment effects

dCauchy() pCauchy() rCauchy() qCauchy(): Cauchy distribution

dCauchyMix() pCauchyMix() rCauchyMix() qCauchyMix(): Cauchy mixture distribution

dcauchymix() pcauchymix() qcauchymix() rcauchymix(): Lowercase vectorized Cauchy mixture distribution functions

causal_alt_pos500_p3_k3: causal_alt_pos500_p3_k3 dataset

causal_alt_pos500_p5_k4_tail: causal_alt_pos500_p5_k4_tail dataset

causal_alt_real500_p4_k2: causal_alt_real500_p4_k2 dataset

causal_pos500_p3_k2: causal_pos500_p3_k2 dataset

check_glue_validity(): Validate bulk+tail glue for MixGPD predictive distribution

cqte(): Conditional quantile treatment effects

dpmgpd.causal(): Fit a causal two-arm Dirichlet process mixture with a spliced GPD tail

dpmgpd.cluster(): Fit a clustering-only bulk-tail model

dpmgpd(): Fit a one-arm Dirichlet process mixture with a spliced GPD tail

dpmix.causal(): Fit a causal two-arm Dirichlet process mixture without a GPD tail

dpmix.cluster(): Fit a clustering-only bulk model

dpmix(): Fit a one-arm Dirichlet process mixture without a GPD tail

ess_summary(): Effective sample size summaries for fitted models

fitted(<mixgpd_fit>): Fitted values on the training design

dGammaGpd() pGammaGpd() rGammaGpd() qGammaGpd(): Gamma with a GPD tail

dgammamix() pgammamix() qgammamix() rgammamix() dgammamixgpd() pgammamixgpd() qgammamixgpd() rgammamixgpd() dgammagpd() pgammagpd() qgammagpd() rgammagpd(): Lowercase vectorized gamma distribution functions

dGammaMix() pGammaMix() rGammaMix() qGammaMix(): Gamma mixture distribution

dGammaMixGpd() pGammaMixGpd() rGammaMixGpd() qGammaMixGpd(): Gamma mixture with a GPD tail

get_kernel_registry(): Get kernel registry

get_tail_registry(): Get tail registry

dGpd() pGpd() rGpd() qGpd(): Generalized Pareto distribution

init_kernel_registry(): Initialize kernel registries

dInvGauss() pInvGauss() rInvGauss() qInvGauss(): Inverse Gaussian (Wald) distribution

dInvGaussGpd() pInvGaussGpd() rInvGaussGpd() qInvGaussGpd(): Inverse Gaussian with a GPD tail

dinvgaussmix() pinvgaussmix() qinvgaussmix() rinvgaussmix() dinvgaussmixgpd() pinvgaussmixgpd() qinvgaussmixgpd() rinvgaussmixgpd() dinvgaussgpd() pinvgaussgpd() qinvgaussgpd() rinvgaussgpd(): Lowercase vectorized inverse Gaussian distribution functions

dInvGaussMix() pInvGaussMix() rInvGaussMix() qInvGaussMix(): Inverse Gaussian mixture distribution

dInvGaussMixGpd() pInvGaussMixGpd() rInvGaussMixGpd() qInvGaussMixGpd(): Inverse Gaussian mixture with a GPD tail

kernel_support_table(): Kernel support matrix

dLaplaceGpd() pLaplaceGpd() rLaplaceGpd() qLaplaceGpd(): Laplace with a GPD tail

dlaplacemix() plaplacemix() qlaplacemix() rlaplacemix() dlaplacemixgpd() plaplacemixgpd() qlaplacemixgpd() rlaplacemixgpd() dlaplacegpd() plaplacegpd() qlaplacegpd() rlaplacegpd(): Lowercase vectorized Laplace distribution functions

dLaplaceMix() pLaplaceMix() rLaplaceMix() qLaplaceMix(): Laplace (double exponential) mixture distribution

dLaplaceMixGpd() pLaplaceMixGpd() rLaplaceMixGpd() qLaplaceMixGpd(): Laplace mixture with a GPD tail

dLognormalGpd() pLognormalGpd() rLognormalGpd() qLognormalGpd(): Lognormal with a GPD tail

dlognormalmix() plognormalmix() qlognormalmix() rlognormalmix() dlognormalmixgpd() plognormalmixgpd() qlognormalmixgpd() rlognormalmixgpd() dlognormalgpd() plognormalgpd() qlognormalgpd() rlognormalgpd(): Lowercase vectorized lognormal distribution functions

dLognormalMix() pLognormalMix() rLognormalMix() qLognormalMix(): Lognormal mixture distribution

dLognormalMixGpd() pLognormalMixGpd() rLognormalMixGpd() qLognormalMixGpd(): Lognormal mixture with a GPD tail

mcmc(): Run posterior sampling from a prepared bundle

nc_pos200_k3: nc_pos200_k3 dataset

nc_posX100_p3_k2: nc_posX100_p3_k2 dataset

nc_posX100_p4_k3: nc_posX100_p4_k3 dataset

nc_posX100_p5_k4: nc_posX100_p5_k4 dataset

nc_pos_tail200_k4: nc_pos_tail200_k4 dataset

nc_real200_k2: nc_real200_k2 dataset

nc_realX100_p3_k2: nc_realX100_p3_k2 dataset

nc_realX100_p5_k3: nc_realX100_p5_k3 dataset

dNormGpd() pNormGpd() rNormGpd() qNormGpd(): Normal with a GPD tail

dnormmix() pnormmix() qnormmix() rnormmix() dnormmixgpd() pnormmixgpd() qnormmixgpd() rnormmixgpd() dnormgpd() pnormgpd() qnormgpd() rnormgpd(): Lowercase vectorized normal distribution functions

dNormMix() pNormMix() rNormMix() qNormMix(): Normal mixture distribution

dNormMixGpd() pNormMixGpd() rNormMixGpd() qNormMixGpd(): Normal mixture with a GPD tail

params(): Extract posterior mean parameters in natural shape

plot(<causalmixgpd_ate>): Plot ATE-style effect summaries

plot(<causalmixgpd_causal_fit>): Plot the treated and control outcome fits from a causal model

plot(<causalmixgpd_causal_predict>): Plot causal prediction outputs

plot(<causalmixgpd_qte>): Plot QTE-style effect summaries

plot(<dpmixgpd_cluster_bundle>): Plot a cluster bundle

plot(<dpmixgpd_cluster_fit>): Plot a cluster fit

plot(<dpmixgpd_cluster_labels>): Plot cluster labels

plot(<dpmixgpd_cluster_psm>): Plot a cluster posterior similarity matrix

plot(<mixgpd_fit>): Plot MCMC diagnostics for a MixGPD fit (ggmcmc backend)

plot(<mixgpd_fitted>): Plot fitted values diagnostics

plot(<mixgpd_predict>): Plot prediction results

predict(<causalmixgpd_causal_fit>): Predict arm-specific and contrast-scale quantities from a causal fit

predict(<dpmixgpd_cluster_fit>): Predict labels or similarity matrices from a cluster fit

predict(<mixgpd_fit>): Posterior predictive summaries from a fitted one-arm model

print(<causalmixgpd_ate>): Print an ATE-style effect object

print(<causalmixgpd_bundle>): Print a one-arm workflow bundle

print(<causalmixgpd_causal_bundle>): Print a causal workflow bundle

print(<causalmixgpd_causal_fit>): Print a fitted causal model

print(<causalmixgpd_causal_fit_plots>): Print method for paired causal-fit diagnostic plots

print(<causalmixgpd_causal_predict_plots>): Print method for causal prediction plots

print(<causalmixgpd_ps_bundle>): Print a propensity score bundle

print(<causalmixgpd_ps_fit>): Print a propensity score fit

print(<causalmixgpd_qte>): Print a QTE-style effect object

print(<dpmixgpd_cluster_bundle>): Print a cluster bundle

print(<dpmixgpd_cluster_fit>): Print a cluster fit

print(<dpmixgpd_cluster_labels>): Print cluster labels

print(<dpmixgpd_cluster_psm>): Print a cluster posterior similarity matrix

print(<mixgpd_fit>): Print a one-arm fitted model

print(<mixgpd_fitted_plots>): Print method for fitted value plots

print(<mixgpd_fit_plots>): Print method for mixgpd_fit diagnostic plots

print(<mixgpd_predict_plots>): Print method for prediction plots

print(<mixgpd_summary>): Print a MixGPD summary object

print(<summary.causalmixgpd_ate>): Print an ATE summary

print(<summary.causalmixgpd_causal_fit>): Print a causal-model summary object

print(<summary.causalmixgpd_ps_fit>): Print a propensity-score summary object

print(<summary.causalmixgpd_qte>): Print a QTE summary

qte(): Quantile treatment effects, marginal over the empirical covariate distribution

qtt(): Quantile treatment effects standardized to treated covariates

residuals(<mixgpd_fit>): Residual diagnostics on the training design

run_mcmc_bundle_manual(): Run posterior sampling for a prepared one-arm bundle

run_mcmc_causal(): Run posterior sampling for a causal bundle

sim_bulk_tail(): Simulate positive bulk-tail data

sim_causal_qte(): Simulate causal quantile-treatment-effect data

sim_survival_tail(): Simulate censored survival-style tail data

summary(<causalmixgpd_ate>): Summarize an ATE-style effect object

summary(<causalmixgpd_bundle>): Summarize a one-arm workflow bundle

summary(<causalmixgpd_causal_bundle>): Summarize a causal workflow bundle

summary(<causalmixgpd_causal_fit>): Summarize a fitted causal model

summary(<causalmixgpd_ps_fit>): Summarize a propensity score fit

summary(<causalmixgpd_qte>): Summarize a QTE-style effect object

summary(<dpmixgpd_cluster_bundle>): Summarize a cluster bundle

summary(<dpmixgpd_cluster_fit>): Summarize a cluster fit

summary(<dpmixgpd_cluster_labels>): Summarize cluster labels

summary(<dpmixgpd_cluster_psm>): Summarize a cluster posterior similarity matrix

summary(<mixgpd_fit>): Summarize posterior draws from a one-arm fitted model