model validation (only internal validation) based on Wagner-Nelson
Method (only for the one-compartment PK model) for Level A correlation
Packaged: 2014-09-23 05:14:23 UTC; datawalker

Title: A Data Analysis Tool for In Vitro-In Vivo Correlation (IVIVC)
Imports: optimx, sciplot, png, grid, deSolve, minpack.lm
Packaged: 2014-11-21 08:06:52 UTC; datawalker

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

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

Title: Statistical Inference and Sensitivity Analysis for Instrumental

Description: Contains functions for carrying out instrumental variable
estimation of causal effects, including power analysis, sensitivity analysis,
Packaged: 2016-05-09 08:17:33 UTC; Hyunseung

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

Packaged: 2017-08-18 16:37:43 UTC; qingyuanzhao

Author: Hyunseung Kang, Yang Jiang, Qingyuan Zhao, and Dylan Small
Description: Carries out instrumental variable
and diagnostics. See Kang, Jiang, Zhao, and Small (2020) <http://pages.cs.wisc.edu/~hyunseung/> for details.
Packaged: 2020-02-20 18:52:14 UTC; hyunseung

Packaged: 2021-01-14 00:56:31 UTC; hyunseung

Title: Instrumental Variables: Extrapolation by Marginal Treatment
Description: The marginal treatment effect was introduced by Heckman and
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
Imports: polynom (>= 1.3-9), Formula, methods, stats, utils
cplexAPI (>= 1.3.3), lpSolve (>= 5.6.13), lpSolveAPI (>=