Methods for Closed Testing with Simes Inequality, in Particular Hommel’s Method (hommel)
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel’s multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented.

Spectral Decomposition of Connectedness Measures (frequencyConnectedness)
Accompanies a paper (Barunik, Krehlik (2017) <doi:10.2139/ssrn.2627599>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://…/frequencyConnectedness> ) for relevant information.

Regression with NA Values in Unordered Factors (nauf)
Fits regressions where unordered factors can be set to NA in subsets of the data where they are not applicable or otherwise not contrastive by using sum contrasts and setting NA values to zero.

Random Partition Distribution Indexed by Pairwise Information (shallot)
Implementations are provided for the models described in the paper D. B. Dahl, R. Day, J. Tsai (2017), ‘Random Partition Distribution Indexed by Pairwise Information,’ Journal of the American Statistical Association, accepted. The Ewens, Ewens-Pitman, Ewens attraction, Ewens-Pitman attraction, and ddCRP distributions are available for prior simulation. We hope in the future to add posterior simulation with a user-supplied likelihood. Supporting functions for partition estimation and plotting are also planned.

Sensitivity Analysis for Observational Studies with Multiple Outcomes (sensitivitymult)
Sensitivity analysis for multiple outcomes in observational studies. For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>. Alternatively, attention may focus on a few principal components in the principal() function. The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.

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