Implements standard and reference based multiple imputation methods for continuous
    longitudinal endpoints (Gower-Page et al. (2022) <doi:10.21105/joss.04251>). In particular,
    this package supports deterministic conditional mean imputation and jackknifing as described
    in Wolbers et al.  (2022) <doi:10.1002/pst.2234>, Bayesian multiple imputation as described
    in Carpenter et al. (2013) <doi:10.1080/10543406.2013.834911>, and bootstrapped maximum
    likelihood imputation as described in von Hippel and Bartlett (2021) <doi: 10.1214/20-STS793>.

Provides utility functions that extend the capabilities of the 
    reference-based multiple imputation package 'rbmi'. It supports clinical 
    trial analysis workflows with functions for managing imputed datasets, 
    applying analysis methods across imputations, and tidying results for 
    reporting.

Creation, manipulation, simulation of linear Gaussian Bayesian
             networks from text files and more...

Provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.

Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.

Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect.  Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.