An implementation of the Invariance Partial Pruning (IVPP) approach 
  described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial 
  Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a 
  two-step method that first test for global network structural difference with 
  invariance test and then inspect specific edge difference with partial pruning.

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

Provides a library for generic interval manipulations using a
    new interval vector class. Capabilities include: locating various
    kinds of relationships between two interval vectors, merging overlaps
    within a single interval vector, splitting an interval vector on its
    overlapping endpoints, and applying set theoretical operations on
    interval vectors. Many of the operations in this package were inspired
    by James Allen's interval algebra, Allen (1983)
    <doi:10.1145/182.358434>.

Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.

Drawing statistical inference on the coefficients
    of a short- or long-horizon predictive regression with persistent
    regressors by using the IVX method of Magdalinos and Phillips (2009)
    <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and
    Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.

For a single variable, the IVY Plot stacks tied values in the form of leaflets. Five leaflets join to form a leaf. 
    Leaves are stacked vertically. At most twenty leaves are shown; For high frequency, each leaflet may represent more than 
    one observation with multiplicity declared in the subtitle.