groupby

<code>data.frame</code> to which the function will assign
groups to each row.

data

vector of the variable/column names in <code>data</code>
that will be used to determine the groups.

variables

name of the column that will be generated to
indicate the group.

groupname

name of the column that will provide a cumulative
count of rows in each group.

countname

boolean switch, set to <code>TRUE</code> if the column
<code>countname</code> should be generated.

count

Auxiliary function that takes in <code>data</code>, and assigns a group
number to all observations with the same entries in the columns
listed in the vector <code>variables</code>.

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.

groupby: Auxiliary function: grouping rows in data

Description

Usage

Arguments

Value