Package index • propertee

StudySpecification

Creation of `StudySpecification` Objects

rct_spec() rd_spec() obs_spec() rct_specification() rd_specification() obs_specification() obsstudy_spec() obsstudy_specification(): Generates a StudySpecification object with the given specifications.

unit_of_assignment() unitid() cluster() uoa() block() forcing(): Special terms in StudySpecification creation formula

as_rct_spec() as_obs_spec() as_rd_spec(): Convert StudySpecification between types

Accessors/Replacers for `StudySpecification` Objects

treatment() `treatment<-`() units_of_assignment() `units_of_assignment<-`() clusters() `clusters<-`() unitids() `unitids<-`() blocks() `blocks<-`() has_blocks() forcings() `forcings<-`(): Accessors and Replacers for StudySpecification objects

has_binary_treatment(): Check whether treatment stored in a StudySpecification object is binary

Summary and print methods

show(<StudySpecification>): Show a StudySpecification

summary(<StudySpecification>) print(<summary.StudySpecification>): Summarizing StudySpecification objects

Structure of `StudySpecification` objects

get_structure() show(<StudySpecificationStructure>): StudySpecification Structure Information

specification_table() stable(): Table of elements from a StudySpecification

var_table() var_names(): Extract Variable Names from StudySpecification

Utility Functions for `StudySpecification` Objects

identify_small_blocks(): Identify fine strata

specification_data_concordance(): Check for variable agreement within units of assignment

identical_StudySpecifications(): Test equality of two StudySpecification objects

Weights

Functions to create or interact with Weights

ett() att() ate() etc() atc() ato() olw() owt() pwt(): Generate Direct Adjusted Weights for Treatment Effect Estimation

Working with `WeightedStudySpecification` objects

weights(<WeightedStudySpecification>): Extract Weights from WeightedStudySpecification

`+`(<WeightedStudySpecification>,<numeric>) `+`(<numeric>,<WeightedStudySpecification>) `-`(<WeightedStudySpecification>,<numeric>) `-`(<numeric>,<WeightedStudySpecification>) `*`(<WeightedStudySpecification>,<numeric>) `*`(<numeric>,<WeightedStudySpecification>) `/`(<WeightedStudySpecification>,<numeric>) `/`(<numeric>,<WeightedStudySpecification>): WeightedStudySpecification Operations

c(<WeightedStudySpecification>): Concatenate weights

subset(<WeightedStudySpecification>) `[`(<WeightedStudySpecification>): WeightedStudySpecification subsetting

show(<WeightedStudySpecification>): Show a WeightedStudySpecification

Covariance Adjustment & SandwichLayer

cov_adj(): Covariance adjustment of teeMod model estimates

as.SandwichLayer(): Convert a PreSandwichLayer to a SandwichLayer with a StudySpecification object

subset(<PreSandwichLayer>) `[`(<PreSandwichLayer>): PreSandwichLayer and SandwichLayer subsetting

show(<PreSandwichLayer>): Show a PreSandwichLayer or SandwichLayer

bread(<teeMod>): Extract bread matrix from a teeMod model fit

estfun(<teeMod>): Extract empirical estimating equations from a teeMod model fit

estfun(<lmrob>) bread(<lmrob>): Generate matrix of estimating equations for lmrob() fit

estfun(<glmrob>) bread(<glmrob>): Extract empirical estimating equations from a glmbrob model fit

Estimating Model

Functions to carry out the treatment estimation accounting for the StudySpecification

lmitt(): Linear Model for Intention To Treat

assigned() adopters() a.() z.(): Obtain Treatment from StudySpecification

as.lmitt() as.teeMod(): Convert lm object into teeMod

confint(<teeMod>): Confidence intervals with standard errors provided by vcov.teeMod()

show(<teeMod>): Show a teeMod

summary(<teeMod>) print(<summary.teeMod>): Summarizing teeMod objects

vcov(<teeMod>): Compute variance-covariance matrix for fitted teeMod model

vcov_tee() .vcov_DB0() .vcov_DB(): Variance/Covariance for teeMod objects

Data

STARplus: STAR participants plus nonexperimental controls

lsoSynth: Synthethic Regression Discontinuity Data

schooldata studentdata: Student data

simdata: Simulated data

michigan_school_pairs: Intervention data from a pair-matched study of schools in Michigan

GV_data: Cluster-randomized experiment data on voter turnout in cable system markets