Author: Original S functions written by Janet E. Heffernan with R port
and R documentation provided by Alec G. Stephenson.

Author: Original S functions written by Janet E. Heffernan with R port and R
documentation provided by Alec G. Stephenson.

Title: Index of Sensitivity to Nonignorability
Author: Hui Xie <huixie@uic.edu>, Weihua Gao, Baodong Xing, Daniel Heitjan, Donald Hedeker, Chengbo Yuan
Description: The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the linear mixed model for longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For more details, see Troxel Ma and Heitjan (2004) and Xie and Heijan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Depends: R (>= 3.0.1), nlme, mvtnorm, nnet, numDeriv,matrixcalc

Title: Index of Local Sensitivity to Nonignorability
Description: The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the linear mixed model for longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <http://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Imports: nlme, mvtnorm, nnet, matrixcalc, Formula