c(person("Laura", "Wendelberger", role = "aut"),
person("Josh", "Gray", role = "aut"),
person("Brian", "Reich", role = "aut"),
person("Alyson", "Wilson", role = "aut"),
person("Shannon T.", "Holloway", role = c("aut", "cre"),
email = "shannon.t.holloway@gmail.com"))
Laura Wendelberger [aut],
Josh Gray [aut],
Brian Reich [aut],
Alyson Wilson [aut],
Shannon T. Holloway [aut, cre]
An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) <arXiv:0710.3742>) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) <arXiv:2112.12899>). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points.
'RcppExports.R' 'checkTypes.R' 'regressionInit.R'
'initRoboBayes.R' 'verifyAnalysisSettings.R' 'roboBayes.R'

Robustness Checks for Omitted Variable Bias
c(
person(given = "Sergei",
family = "Schaub",
role = c("aut", "cre"),
email = "seschaub@ethz.ch",
comment = c(ORCID = "0000-0001-8477-3737")),
person("ETH Zurich",
role = c("cph"))
)
Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). 'robomit' replicates (and extends) function available in Stata (closed source) for use in R (open source). See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>.
plm, dplyr, ggplot2, broom, tidyr, tibble, stats,
Sergei Schaub [aut, cre] (<https://orcid.org/0000-0001-8477-3737>),
ETH Zurich [cph]

Robustness checks for omitted variable bias. The package includes robustness checks proposed by Oster (2019). robomit the estimate i) the bias-adjusted treatment correlation or effect and ii) the degree of selection on unobservables relative to observables (with respect to the treatment variable) that would be necessary to eliminate the result based on the framework by Oster (2019). Additionally, robomit offers a set of sensitivity analysis and visualization functions. See: Oster, E. 2019. <doi:10.1080/07350015.2016.1227711>.