A Data Analysis Tool for In Vitro-In Vivo Correlation (IVIVC)
optimx, sciplot, png, grid, deSolve, minpack.lm

Uses heteroscedasticity to estimate mismeasured and endogenous
regressor models
GMM estimation of triangular systems using heteroscedasticity based instrumental variables as in Lewbel (2012)

Statistical Inference and Diagnostics for Instrumental Variables
Model
Yang Jiang, Hyunseung Kang, and Dylan Small
Contains functions for carrying out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics.

Statistical Inference and Sensitivity Analysis for Instrumental
Variables Model

Contains functions for carrying out instrumental variable
estimation of causal effects, including power analysis, sensitivity analysis,
and diagnostics.

Yang Jiang, Hyunseung Kang, Dylan Small, and Qingyuan Zhao

Hyunseung Kang, Yang Jiang, Qingyuan Zhao, and Dylan Small
Carries out instrumental variable
estimation of causal effects, including power analysis, sensitivity analysis,
and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.

Instrumental Variables: Extrapolation by Marginal Treatment
Effects
c(person("Alexander",
"Torgovitsky",
email = "atorgovitsky@gmail.com",
role = c("aut")),
person("Joshua",
"Shea",
email = "jkcshea@uchicago.edu",
role = c("aut", "cre")))
The marginal treatment effect was introduced by Heckman and
Vytlacil (2005) <doi:10.1111/j.1468-0262.2005.00594.x> to provide a
choice-theoretic interpretation to instrumental variables models that
maintain the monotonicity condition of Imbens and Angrist (1994)
<doi:10.2307/2951620>. This interpretation can be used to extrapolate from
the compliers to estimate treatment effects for other subpopulations. This
package provides a flexible set of methods for conducting this
extrapolation. It allows for parametric or nonparametric sieve estimation,
and allows the user to maintain shape restrictions such as monotonicity. The
package operates in the general framework developed by Mogstad, Santos and
Torgovitsky (2018) <doi:10.3982/ECTA15463>, and accommodates either point
identification or partial identification (bounds). In the partially
identified case, bounds are computed using linear programming. Support for
three linear programming solvers is provided. Gurobi and the Gurobi R API
can be obtained from <http://www.gurobi.com/index>. CPLEX can be obtained
from <https://www.ibm.com/analytics/cplex-optimizer>. CPLEX R APIs 'Rcplex'
and 'cplexAPI' are available from CRAN. The lp_solve library is freely
available from <http://lpsolve.sourceforge.net/5.5/>, and is included when
installing either of its R APIs, 'lpSolve' or 'lpSolveAPI', which are
available from CRAN.
polynom (>= 1.3-9), Formula, methods, stats, utils
Alexander Torgovitsky [aut],
Joshua Shea [aut, cre]