Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Various opportunities to evaluate the effects of including one or more control variable(s) in structural equation models onto model-implied variances, covariances, and parameter estimates. The derivation of the methodology employed in this package can be obtained from Blötner (2023) <doi:10.31234/osf.io/dy79z>.

A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.

Estimating causal parameters in the presence of treatment spillover is of great interest in statistics.
    This package provides tools for instrumental variables estimation of average causal effects under network interference of unknown form.
    The target parameters are the local average direct effect, the local average indirect effect, the local average overall effect, and the local average spillover effect.
    The methods are developed by Hoshino and Yanagi (2023) <doi:10.48550/arXiv.2108.07455>.

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
             comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
             The estimation is based on latent copula Gaussian models.
             For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
             For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>.
             For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
             For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.