ggplot2 (>= 3.3.5), gridExtra (>= 2.3), stats, utils, methods

ggplot2 (>= 3.3.5), stats, utils, methods, ggnewscale, ggpubr,

Title: Conformal Inference Tools for Regression with Multivariate
conformal prediction regions when the response variable is
multivariate (i.e. dimension is greater than one). Moreover, the
package also contain plot functions to visualize the output of the
To guarantee consistency, the package structure mimics the univariate 'conformalInference'
Imports: future (>= 1.23.0), future.apply (>= 1.8.1), ggplot2 (>=
3.3.5), glmnet, gridExtra (>= 2.3), hrbrthemes, stats, utils

3.3.5), glmnet, gridExtra (>= 2.3), hrbrthemes, stats,

Title: Jackknife(+) Predictive Intervals for Bayesian Models
person("Cory", "McCartan", , "cmccartan@g.harvard.edu", role = c("aut", "cre"),
Description: Provides functions to construct finite-sample calibrated predictive
intervals for Bayesian models, following the approach in Barber et al.
(2021) <doi:10.1214/20-AOS1965>. These intervals are calculated efficiently
using importance sampling for the leave-one-out residuals. By default,
the intervals will also reflect the relative uncertainty in the Bayesian
model, using the locally-weighted conformal methods of Lei et al. (2018)
Imports: cli, rstantools, loo, matrixStats
Suggests: rstanarm, brms, testthat (>= 3.0.0), ggplot2, knitr,
Packaged: 2022-03-09 20:20:07 UTC; cmccartan
Author: Cory McCartan [aut, cre] (<https://orcid.org/0000-0002-6251-669X>)

Packaged: 2022-08-12 07:55:01 UTC; cmccartan

Title: Diagnostics for Confounding of Time-Varying and Other Joint
Authors@R: c(person(given = "John W.", family = "Jackson",email = "john.jackson@jhu.edu", role = c("aut", "cre")),person(given =  "Erin", family = "Schnellinger",role = c("ctb")),person(given =  "Linda", family = "Valeri",role = c("ctb")))
Maintainer: John W. Jackson <john.jackson@jhu.edu>
Description: Implements three covariate-balance diagnostics for time-varying confounding and selection-bias in complex longitudinal data, as described in Jackson (2016) <doi:10.1097/EDE.0000000000000547> and Jackson (2019) <doi:10.1093/aje/kwz136>. Diagnostic 1 assesses measured confounding/selection-bias, diagnostic 2 assesses exposure-covariate feedback, and diagnostic 3 assesses residual confounding/selection-bias after inverse probability weighting or propensity score stratification. All diagnostics appropriately account for exposure history, can be adapted to assess a particular depth of covariate history, and can be implemented in right-censored data. Balance assessments can be obtained for all times, selected-times, or averaged across person-time. The balance measures are reported as tables or plots. These diagnostics can be applied to the study of multivariate exposures including time-varying exposures, direct effects, interaction, and censoring.
Imports: grid,gridExtra (>= 2.3),scales (>= 0.5.0),Rmpfr (>=