Sensitivity to unmeasured biases in an observational study that is a full match.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.

Implements exact, normally approximated, and sampling-based sensitivity 
    analysis for observational studies with contingency tables. Includes exact 
    (kernel-based), normal approximation, and sequential importance sampling (SIS) 
    methods using 'Rcpp' for computational efficiency. The methods build upon the 
    framework introduced in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2> and 
    the generalized design sensitivity framework developed by Chiu (2025) 
    <doi:10.48550/arXiv.2507.17207>.

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.

The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean.  The main function in the package is senmv(), but amplify() and truncatedP() are also useful.  The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.

Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test).   The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.