Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective (naivereg)
In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case. The package incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection. It is nonparametric version of ‘ivregress’ in ‘Stata’ with IV selection and high dimensional features. The package is based on the paper ‘Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective’ (2017) published online in Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991>.

Index of Sensitivity to Nonignorability (isni)
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>.

Chandler-Bate Sandwich Loglikelihood Adjustment (chandwich)
Performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters.

Variable Life Adjusted Display (vlad)
Contains functions to set up risk-adjusted quality control charts in health care. For the variable life adjusted display (VLAD) proposed by Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> and the risk-adjusted cumulative sum chart based on log-likelihood ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441> the average run length and control limits can be computed.

Whitening and High-Dimensional Canonical Correlation Analysis (whitening)
Implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) ‘Optimal whitening and decorrelation’, The American Statistician, <doi:10.1080/00031305.2016.1277159>, as well as the whitening approach to Canonical Correlation Analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2018) ‘Probabilistic canonical correlation analysis: a whitening approach’, <arXiv:1802.03490>.