Clean up repo (#30)

* Added Rglpk as a dependency * Removed tidyverse as package loading * Added mvtnorm as dependency * Squashed commit of the following: commit 3c3c4c53588b390f7ca8d5c78d5dff5cae7dc5ec Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Wed Apr 5 14:53:19 2023 -0400 More explicit calls commit 9d01fe1 Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Sat Apr 1 20:58:22 2023 -0400 Re-compiled from R studio; closes #24 commit b8cf9f2 Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Sat Apr 1 20:42:14 2023 -0400 Added explicit calls to built-ins * Squashed commit of the following: commit b9d8af8 Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Thu May 11 01:07:47 2023 -0400 Moved intro of staggered example into README commit 02b541b Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Thu May 11 01:01:11 2023 -0400 Fixed potential integer overflow commit b4767b7 Author: Mauricio Caceres <mauricio.caceres.bravo@gmail.com> Date: Thu May 11 00:56:23 2023 -0400 Cleaned and moved honest_did function to own file * Several fixes/edits to pass CRAN check - Added matrixStats to Imports - Deleted ROI - Renamed doc -> vignettes - .compute_IDset_DeltaRMB_fixedS: biasDrection -> biasDirection - .create_A_M: A_I -> A_M - DeltaSD_upperBound_Mpre: stata -> stats - Changed title to Title Case - https://jonathandroth.github.io -> https://www.jonathandroth.com/ - Aligned man files with function definitions: - findOptimalFLCI - createSensitivityResults_relativeMagnitudes - computeConditionalCS_DeltaSDRMB - computeConditionalCS_DeltaSDM - computeConditionalCS_DeltaSDB - computeConditionalCS_DeltaSD - Added deltaSD.png, README to .Rbuildignore - Added VignetteBuilder and knitr to DESCRIPTION * Fixed Ashesh e-mail; removed library statements * Bumped version # * Added rmarkdown to build vignettes * Deleted keyword placeholders; moved doParallel to suggested packages
asheshrambachan · May 30, 2023 · a1e7126 · a1e7126
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -1,2 +1,4 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+README
+deltaSD.png
diff --git a/.gitignore b/.gitignore
@@ -1,4 +1,5 @@
 .Rproj.user
+*.Rcheck
 .Rhistory
 .RData
 .Ruserdata

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,29 +1,35 @@
 Package: HonestDiD
 Type: Package
-Title: Robust inference in difference-in-differences and event study designs
-Version: 0.2.2
+Title: Robust Inference in Difference-in-Differences and Event Study Designs
+Version: 0.2.3
 Depends:
     R (>= 3.6.0)
 Imports:
+    stats,
     foreach (>= 1.4.7),
-    doParallel (>= 1.0.15),
+    matrixStats (>= 0.63.0),
     CVXR (>= 0.99-6),
     latex2exp (>= 0.4.0),
     lpSolveAPI (>= 5.5.2.0-17),
     Matrix (>= 1.2-17),
     pracma (>= 2.2.5),
     purrr (>= 0.3.4),
-    ROI (>= 0.3-2),
     tibble (>= 1.3.4),
     dplyr (>= 0.7.4),
     ggplot2 (>= 2.2.1),
     Rglpk (>= 0.6-4),
     mvtnorm (>= 1.1-3),
     TruncatedNormal (>= 1.0)
-Author: Ashesh Rambachan
-Maintainer: Ashesh Rambachan <asheshr@g.harvard.edu>
-Description: 
-    This package provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2021). Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets. See Ashesh Rambachan & Jonathan Roth, "An Honest Approach to Parallel Trends", 2021 for details on the methods.
+Suggests:
+    doParallel (>= 1.0.15),
+    knitr,
+    rmarkdown
+Author: Ashesh Rambachan <ashesh.a.rambachan@gmail.com>
+Maintainer: Ashesh Rambachan <ashesh.a.rambachan@gmail.com>
+Description:
+    Provides functions to conduct robust inference in difference-in-differences and event study designs by implementing the methods developed in Rambachan & Roth (2021). Inference is conducted under a weaker version of the parallel trends assumption. Uniformly valid confidence sets are constructed based upon conditional confidence sets, fixed-length confidence sets and hybridized confidence sets. See Ashesh Rambachan & Jonathan Roth, "An Honest Approach to Parallel Trends", 2021 for details on the methods.
 License: GPL-3
 Encoding: UTF-8
 LazyData: true
+VignetteBuilder:
+    knitr
diff --git a/R/HonestDiD-Temp.R b/R/HonestDiD-Temp.R
@@ -17,7 +17,6 @@
 # PRELIMINARIES =======================================================
 library(TruncatedNormal)
 library(lpSolveAPI)
-library(ROI)
 library(Matrix)
 library(pracma)
 library(CVXR)

diff --git a/R/arp-nuisance.R b/R/arp-nuisance.R
@@ -8,15 +8,6 @@
 #  This script contains functions that are used to construct
 #  the ARP test with nuisance parameters.
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # ARP HELPER FUNCTIONS ------------------------------------------------
 .norminvp_generalized <- function(p, l, u, mu = 0, sd = 1){
   lnormalized <- (l-mu)/sd
@@ -90,7 +81,7 @@ library(foreach)
 
   if (base::is.na(checksol) || !checksol) {
     # warning('User-supplied eta is not a solution. Not rejecting automatically')
-    base::return( list(vlo = eta, vup = Inf) )
+    base::return( base::list(vlo = eta, vup = Inf) )
   }
 
   ### Compute vup ###

diff --git a/R/delta_utility_functions.R b/R/delta_utility_functions.R
@@ -32,7 +32,7 @@
   # If postPeriodMomentsOnly == T, exclude moments that only involve pre-periods
   if(postPeriodMomentsOnly){
     postPeriodIndices <- (numPrePeriods +1):base::NCOL(A_M)
-    prePeriodOnlyRows <- base::which( base::rowSums( A_I[ , postPeriodIndices] != 0 ) == 0 )
+    prePeriodOnlyRows <- base::which( base::rowSums( A_M[ , postPeriodIndices] != 0 ) == 0 )
     A_M <- A_M[-prePeriodOnlyRows , ]
   }
   if (monotonicityDirection == "decreasing") {

diff --git a/R/deltarm.R b/R/deltarm.R
@@ -8,16 +8,6 @@
 #  This script contains functions that are used to construct
 #  the confidence sets for Delta^{RM}(Mbar).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # Delta^{RM} functions -----------------------------------------------
 .create_A_RM <- function(numPrePeriods, numPostPeriods,
                          Mbar = 1, s, max_positive = T,

diff --git a/R/deltarmb.R b/R/deltarmb.R
@@ -10,16 +10,6 @@
 #  with a sign restriction (i.e., Delta^{PB} or Delta^{NB}). See the discussion
 #  Section 2.3.3 of Rambachan and Roth (2021).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # Delta^{RMB} functions -----------------------------------------------
 .create_A_RMB <- function(numPrePeriods, numPostPeriods,
                           Mbar = 1, s, max_positive = T,
@@ -107,7 +97,7 @@ library(purrr)
 
   # Create A_RM, d_RM for this choice of s, max_positive
   A_RMB_s = .create_A_RMB(numPrePeriods = numPrePeriods, numPostPeriods = numPostPeriods,
-                          Mbar = Mbar, s = s, max_positive = max_positive, biasDirection = biasDrection)
+                          Mbar = Mbar, s = s, max_positive = max_positive, biasDirection = biasDirection)
   d_RMB = .create_d_RMB(numPrePeriods = numPrePeriods, numPostPeriods = numPostPeriods)
 
   # Create vector for direction of inequalities associated with RM

diff --git a/R/deltarmm.R b/R/deltarmm.R
@@ -9,16 +9,6 @@
 #  the confidence sets for Delta^{RM}(Mbar), which intersects Delta^{RM}(Mbar)
 #  with a shape restriction (i.e., Delta^{I} or Delta^{D}).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # Delta^{RMM} functions -----------------------------------------------
 .create_A_RMM <- function(numPrePeriods, numPostPeriods,
                          Mbar = 1, s, max_positive = T,

diff --git a/R/deltasd.R b/R/deltasd.R
@@ -8,15 +8,6 @@
 #  This script contains functions that are used to construct
 #  the confidence sets for Delta^{SD}(M).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # DELTA^{SD}(M) FUNCTIONS ---------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SD}(M) into the form needed to use the ARP functions.
@@ -136,10 +127,11 @@ library(foreach)
 }
 
 computeConditionalCS_DeltaSD <- function(betahat, sigma, numPrePeriods, numPostPeriods,
-                                         l_vec = .basisVector(index = 1, size = numPostPeriods), M = 0,
-                                         alpha = 0.05, hybrid_flag = "FLCI", hybrid_kappa = alpha/10,
-                                         returnLength = F, postPeriodMomentsOnly = T,
-                                         gridPoints =10^3, grid.midPoint = NA, grid.ub = NA, grid.lb = NA) {
+                                         l_vec = .basisVector(index = 1, size = numPostPeriods),
+                                         M = 0, alpha = 0.05, hybrid_flag = "FLCI", 
+                                         hybrid_kappa = alpha/10, returnLength = F,
+                                         postPeriodMomentsOnly = T,
+                                         gridPoints =10^3, grid.ub = NA, grid.lb = NA) {
   # This function computes the ARP CI that includes nuisance parameters
   # for Delta^{SD}(M). This functions uses ARP_computeCI for all
   # of its computations.

diff --git a/R/deltasdb.R b/R/deltasdb.R
@@ -8,15 +8,6 @@
 #  This script contains functions that are used to construct
 #  the confidence sets for Delta^{SDB}(M).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # DELTA^{SDB}(M) FUNCTIONS --------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SDB}(M) into the form needed to use the ARP functions.
@@ -118,15 +109,12 @@ library(foreach)
     id.ub = id.ub))
 }
 
-computeConditionalCS_DeltaSDB <- function(betahat, sigma, numPrePeriods, numPostPeriods, M = 0,
-                                          l_vec = .basisVector(index = 1, size=numPostPeriods),
-                                          alpha = 0.05,
-                                          hybrid_flag = "FLCI", hybrid_kappa = alpha/10,
+computeConditionalCS_DeltaSDB <- function(betahat, sigma, numPrePeriods, numPostPeriods,
+                                          M = 0, l_vec = .basisVector(index = 1, size=numPostPeriods),
+                                          alpha = 0.05, hybrid_flag = "FLCI", hybrid_kappa = alpha/10,
                                           returnLength = F, biasDirection = "positive",
                                           postPeriodMomentsOnly = T,
-                                          gridPoints = 10^3,
-                                          grid.lb = NA,
-                                          grid.ub = NA) {
+                                          gridPoints = 10^3, grid.lb = NA, grid.ub = NA) {
   # This function computes the ARP CI that includes nuisance parameters
   # for Delta^{SDPB}(M). This functions uses ARP_computeCI for all
   # of its computations.

diff --git a/R/deltasdm.R b/R/deltasdm.R
@@ -8,15 +8,6 @@
 #  This script contains functions that are used to construct
 #  the confidence sets for Delta^{SDM}(M).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # DELTA^{SDM}(M) FUNCTIONS --------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SDM}(M) into the form needed to use the ARP functions.
@@ -107,14 +98,12 @@ library(foreach)
     id.ub = id.ub))
 }
 
-computeConditionalCS_DeltaSDM <- function(betahat, sigma, numPrePeriods, numPostPeriods, M = 0,
-                                          l_vec = .basisVector(index = 1, size = numPostPeriods),
+computeConditionalCS_DeltaSDM <- function(betahat, sigma, numPrePeriods, numPostPeriods,
+                                          M = 0, l_vec = .basisVector(index = 1, size = numPostPeriods),
                                           alpha = 0.05, monotonicityDirection = "increasing",
                                           hybrid_flag = "FLCI", hybrid_kappa = alpha/10,
                                           returnLength = F, postPeriodMomentsOnly = T,
-                                          gridPoints=10^3,
-                                          grid.lb = NA,
-                                          grid.ub = NA) {
+                                          gridPoints=10^3, grid.lb = NA, grid.ub = NA) {
   # This function computes the ARP CI that includes nuisance parameters
   # for Delta^{SDI}(M). This functions uses ARP_computeCI for all
   # of its computations.

diff --git a/R/deltasdrm.R b/R/deltasdrm.R
@@ -8,16 +8,6 @@
 #  This script contains functions that are used to construct
 #  the confidence sets for Delta^{SDRM}(Mbar).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # DELTA^{SDRM}(Mbar) FUNCTIONS ---------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SDRM}(Mbar) into the form needed to use the ARP functions.

diff --git a/R/deltasdrmb.R b/R/deltasdrmb.R
@@ -10,16 +10,6 @@
 #  with a sign restriction (i.e., Delta^{PB} or Delta^{NB}). See the discussion
 #  Section 2.3.3 of Rambachan and Roth (2021).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # DELTA^{SDRMB}(Mbar) FUNCTIONS ---------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SDRMB}(Mbar) into the form needed to use the ARP functions.
@@ -260,9 +250,10 @@ library(purrr)
 }
 
 computeConditionalCS_DeltaSDRMB <- function(betahat, sigma, numPrePeriods, numPostPeriods,
-                                            l_vec = .basisVector(index = 1, size = numPostPeriods), Mbar = 0,
-                                            alpha = 0.05, hybrid_flag = "LF", hybrid_kappa = alpha/10,
-                                            returnLength = F, postPeriodMomentsOnly = T, biasDirection = "positive",
+                                            l_vec = .basisVector(index = 1, size = numPostPeriods),
+                                            Mbar = 0, alpha = 0.05, hybrid_flag = "LF",
+                                            hybrid_kappa = alpha/10, returnLength = F,
+                                            postPeriodMomentsOnly = T, biasDirection = "positive",
                                             gridPoints = 10^3, grid.ub = NA, grid.lb = NA) {
   # This function computes the ARP CI that includes nuisance parameters
   # for Delta^{SDRMB}(Mbar). This functions uses ARP_computeCI for all

diff --git a/R/deltasdrmm.R b/R/deltasdrmm.R
@@ -9,16 +9,6 @@
 #  the confidence sets for Delta^{SDRMM}(Mbar), which intersects Delta^{SDRM}(Mbar)
 #  with a shape restriction (i.e., Delta^{I} or Delta^{D}).
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-library(purrr)
-
 # DELTA^{SDRMM}(Mbar) FUNCTIONS ---------------------------------------------
 # In this section, we implement helper functions to place testing with
 # Delta^{SDRMM}(Mbar) into the form needed to use the ARP functions.

diff --git a/R/flci.R b/R/flci.R
@@ -8,15 +8,6 @@
 #  This script contains functions that are used to construct
 #  the FLCI for a general choice of vector l and Delta = Delta^{SD}(M)
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # FLCI HELPER FUNCTIONS -----------------------------------------------
 .createConstraints_AbsoluteValue <- function(sigma, numPrePeriods, UstackW){
   # This function creates linear constraints that help to minimize worst-case bias

diff --git a/R/sensitivityresults.R b/R/sensitivityresults.R
@@ -8,15 +8,6 @@
 #  Implements functions to perform sensitivity analysis on event study coefficients
 
 
-# PRELIMINARIES =======================================================
-library(TruncatedNormal)
-library(lpSolveAPI)
-library(ROI)
-library(Matrix)
-library(pracma)
-library(CVXR)
-library(foreach)
-
 # Construct Robust Results Function for Smoothness Restrictions -----------------------------------
 createSensitivityResults <- function(betahat, sigma,
                                      numPrePeriods, numPostPeriods,
@@ -31,7 +22,7 @@ createSensitivityResults <- function(betahat, sigma,
   # If Mvec is null, construct default Mvec
   if (base::is.null(Mvec)) {
     if (numPrePeriods == 1) {
-      Mvec = base::seq(from = 0, to = base::c(base::sqrt(base::sigma[1, 1])), length.out = 10)
+      Mvec = base::seq(from = 0, to = base::c(base::sqrt(sigma[1, 1])), length.out = 10)
     } else {
       Mub = DeltaSD_upperBound_Mpre(betahat = betahat, sigma = sigma, numPrePeriods = numPrePeriods, alpha = 0.05)
       Mvec = base::seq(from = 0, to = Mub, length.out = 10)
@@ -420,7 +411,9 @@ createSensitivityResults_relativeMagnitudes <- function(betahat, sigma,
                                                         monotonicityDirection = NULL,
                                                         biasDirection = NULL,
                                                         alpha = 0.05,
-                                                        gridPoints = 10^3, grid.ub = NA, grid.lb = NA,
+                                                        gridPoints = 10^3,
+                                                        grid.ub = NA,
+                                                        grid.lb = NA,
                                                         parallel = FALSE) {
 
   # If Mbarvec is null, construct default Mbarvec to be 10 values on [0,2].
@@ -681,7 +674,9 @@ createSensitivityResults_relativeMagnitudes <- function(betahat, sigma,
   base::return(Results)
 }
 
-createSensitivityPlot_relativeMagnitudes <- function(robustResults, originalResults, rescaleFactor = 1, maxMbar = Inf, add_xAxis = TRUE) {
+createSensitivityPlot_relativeMagnitudes <- function(robustResults, originalResults,
+                                                     rescaleFactor = 1, maxMbar = Inf,
+                                                     add_xAxis = TRUE) {
   # Set Mbar for OLS to be the min Mbar in robust results minus the gap between Mbars in robust
   Mbargap <- base::min( base::diff( base::sort( robustResults$Mbar) ) )
   Mbarmin <- base::min( robustResults$Mbar)