documentation issues fixed

DESCRIPTION

@@ -6,7 +6,7 @@ Authors@R: c(
     person("Ties", "Bos", email = "tc.bos@student.maastrichtuniversity.nl", role = c("aut","cre")),
     person("Martin", "Schumann", email = "m.schumann@maastrichtuniversity.nl", role = "ctb"))
 Maintainer: Ties Bos <tc.bos@student.maastrichtuniversity.nl>
-Description: Testing for parallel trends is crucial in the Difference-in-Difference framework. EquiTrends is an R package for equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi: 10.1080/07350015.2024.2308121>. 
+Description: Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>. 
 License: MIT + file LICENSE
 Imports: 
     clubSandwich,

R/EquiTrends_Main_Functions.R

@@ -39,7 +39,7 @@
 #' 
 #' NOTE: Please be aware that including control variables (X) might lead to higher computation times for type = "Boot" and type = "Wild", due to unconstrained parameters in the optimization problem that estimates the constrained placebo coefficients.
 #' 
-#' On top of that, please be aware that the bootstrap procedures for the equivalence test based on the maximum absolute placebo coefficient apply a bootstrap procedure (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary slightly between different runs of the function.
+#' On top of that, please be aware that the bootstrap procedures for the equivalence test based on the maximum absolute placebo coefficient apply a bootstrap procedure (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary slightly between different runs of the function. For reproducibility of the bootstrap procedures, it is recommended to set a seed before using the function.
 #' 
 #' @references
 #' Arellano M (1987). “Computing Robust Standard Errors for Within-groups Estimators.” \emph{Oxford bulletin of Economics and Statistics}, 49(4), 431–434.
@@ -446,7 +446,7 @@ meanEquivTest <- function(Y, ID, G, period, X = NULL, data = NULL, equiv_thresho
 #' 
 #' One should note that rows containing \code{NA} values are removed from the panel before the testing procedure is performed.
 #' 
-#' Please be aware that the equivalence test based on the root mean squared placebo coefficient uses a randomization technique (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary slightly between different runs of the function.
+#' Please be aware that the equivalence test based on the root mean squared placebo coefficient uses a randomization technique (as described by Dette & Schumann (2024)), leading to a stochastic critical value and minimum equivalence threshold. Therefore, the results may vary slightly between different runs of the function. For reproducibility, it is recommended to set a seed before using the function.
 #' 
 #' @seealso \code{\link[=print.rmsEquivTest]{print.rmsEquivTest}}
 #' 

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 [![Codecov test coverage](https://codecov.io/gh/TiesBos/EquiTrends/graph/badge.svg)](https://app.codecov.io/gh/TiesBos/EquiTrends)
 <!-- badges: end -->
 
-EquiTrends is an R package for equivalence testing in the context of Difference-in-Differences
+`EquiTrends` is an R package for equivalence testing in the context of Difference-in-Differences
 estimation. It allows users to test if pre-treatment trends in the treated group are
 “equivalent” to those in the control group. Here, “equivalence” means that rejection of the
 null hypothesis implies that a function of the pre-treatment placebo effects (maximum
@@ -32,7 +32,7 @@ The package contains the functions `maxEquivTest` to perform the testing procedu
 
 ## Installation
 
-You can install the development version of EquiTrends from [GitHub](https://github.com/TiesBos/EquiTrends) with:
+You can install the development version of `EquiTrends` from [GitHub](https://github.com/TiesBos/EquiTrends) with:
 
 ```{r echo=TRUE, message=FALSE, results='hide'}
 # install.packages("devtools")
@@ -41,7 +41,7 @@ devtools::install_github("TiesBos/EquiTrends")
 
 ## Data Simulation
 
-The `EquiTrends` package contains a function to simulate panel data, tailored to the Difference-in-Difference framework. The function `sim_paneldata` simulates a panel dataset with a given number of individuals $N$ (`N`), number of periods $T+1$ (in the setting of this package, indicating the number of pre-treatment periods. In `sim_paneldata` $T+1$ is referred to as `tt`), number of covariates $p$ (`p`), and treatment effects. Typically, period $T+1$ is referred to as the "base period". The function also allows for the simulation of heterogeneity in treatment effects (specified through `eta`) and time fixed effects (through `lambda`). Furthermore, the function allows for heteroscedasticty (specified through the binary variable `het`), serial correlation (through the AR(1) coefficient `phi`: $u_{i,t} = \phi u_{i,t-1} + v_{i,t}$ where $v_{i,t}$ follows an i.i.d. $N(0,\sigma^2)$ distribution and $\sigma$ is specified through `sd`), and clustering in the model errors $u_{i,t}$. The function returns a data frame with the following columns: `ID` (the cross-sectional individual identifier), `period` (the time identifier), `Y` (the dependent variable), `G` (a binary vector indicating if an individual receives treatment, indicated by 1, or not, indicated by 0), and `X_1`, `X_2`, ..., `X_p` (additional control variables). The construction of the dependent variable follows the two-way fixed effect model, similar to the model in equation (2.5) of Dette & Schumann ([2024](https://doi.org/10.1080/07350015.2024.2308121)):
+The `EquiTrends` package contains a function to simulate panel data, tailored to the Difference-in-Differences framework. The function `sim_paneldata` simulates a panel dataset with a given number of individuals $N$ (`N`), number of periods $T+1$ (in the setting of this package, indicating the number of pre-treatment periods. In `sim_paneldata` $T+1$ is referred to as `tt`), number of covariates $p$ (`p`), and treatment effects. Typically, period $T+1$ is referred to as the "base period". The function also allows for the simulation of heterogeneity in treatment effects (specified through `eta`) and time fixed effects (through `lambda`). Furthermore, the function allows for heteroscedasticty (specified through the binary variable `het`), serial correlation (through the AR(1) coefficient `phi`: $u_{i,t} = \phi u_{i,t-1} + v_{i,t}$ where $v_{i,t}$ follows an i.i.d. $N(0,\sigma^2)$ distribution and $\sigma$ is specified through `sd`), and clustering in the model errors $u_{i,t}$. The function returns a data frame with the following columns: `ID` (the cross-sectional individual identifier), `period` (the time identifier), `Y` (the dependent variable), `G` (a binary vector indicating if an individual receives treatment, indicated by 1, or not, indicated by 0), and `X_1`, `X_2`, ..., `X_p` (additional control variables). The construction of the dependent variable follows the two-way fixed effect model, similar to the model in equation (2.5) of Dette & Schumann ([2024](https://doi.org/10.1080/07350015.2024.2308121)):
 
 $$Y_{i,t} =  \eta_i + \lambda_t + \sum_{l=1}^{T}{\beta_l}G_iD_l(t) + X_{1, i, t}\gamma_1+ \dots + X_{p,i,t}\gamma_p +u_{i,t} \quad \text{with} \  \ i=1,...,N, \ \ t=1,...,T+1$$
 
@@ -63,7 +63,7 @@ head(sim_data)
 
 ## Testing for Equivalence of Pre-Trends
 
-The `EquiTrends` package contains functions to test for equivalence of pre-trends in difference-in-differences estimation. The functions `rmsEquivTest`, `meanEquivTest`, and `maxEquivTest` are used to test for equivalence of pre-trends in difference-in-differences estimation using the placebo coefficients $\beta_{l} \ (l=1,...,T)$ estimates. The functions are based on the work of Dette & Schumann ([2024](https://doi.org/10.1080/07350015.2024.2308121)).
+The `EquiTrends` package contains functions to test for equivalence of pre-trends in Difference-in-Differences estimation. The functions `rmsEquivTest`, `meanEquivTest`, and `maxEquivTest` are used to test for equivalence of pre-trends in Difference-in-Differences estimation using the placebo coefficients $\beta_{l} \ (l=1,...,T)$ estimates. The functions are based on the work of Dette & Schumann ([2024](https://doi.org/10.1080/07350015.2024.2308121)).
 
 ### The `rmsEquivTest` function
 `rmsEquivTest` implements the equivalence testing procedure surrounding the root mean squared placebo coefficient as described in section 4.2.3 of Dette & Schumann ([2024](https://doi.org/10.1080/07350015.2024.2308121)). The function tests the null hypothesis that the root mean squared placebo coefficient is larger than or equal to a user-specified equivalence threshold $\delta$. That is, if 

README.md

@@ -12,7 +12,7 @@ MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.or
 coverage](https://codecov.io/gh/TiesBos/EquiTrends/graph/badge.svg)](https://app.codecov.io/gh/TiesBos/EquiTrends)
 <!-- badges: end -->
 
-EquiTrends is an R package for equivalence testing in the context of
+`EquiTrends` is an R package for equivalence testing in the context of
 Difference-in-Differences estimation. It allows users to test if
 pre-treatment trends in the treated group are “equivalent” to those in
 the control group. Here, “equivalence” means that rejection of the null
@@ -37,7 +37,7 @@ simulate a paneldataset for such testing purposes.
 
 ## Installation
 
-You can install the development version of EquiTrends from
+You can install the development version of `EquiTrends` from
 [GitHub](https://github.com/TiesBos/EquiTrends) with:
 
 ``` r
@@ -48,7 +48,7 @@ devtools::install_github("TiesBos/EquiTrends")
 ## Data Simulation
 
 The `EquiTrends` package contains a function to simulate panel data,
-tailored to the Difference-in-Difference framework. The function
+tailored to the Difference-in-Differences framework. The function
 `sim_paneldata` simulates a panel dataset with a given number of
 individuals $N$ (`N`), number of periods $T+1$ (in the setting of this
 package, indicating the number of pre-treatment periods. In
@@ -94,21 +94,21 @@ sim_data <- sim_paneldata(N = 500, tt = 5, p = 2, beta = rep(0, 5),
                           gamma = rep(1, 2), het = 0, phi = 0, sd = 1, 
                           burnins = 50)
 head(sim_data)
-#>   ID period          Y G        X_1         X_2
-#> 1  1      1  0.2360773 0  0.1440742  0.08900155
-#> 2  1      2 -1.2535981 0 -0.3855007 -0.06428798
-#> 3  1      3 -0.9389796 0 -0.3615485 -0.32920657
-#> 4  1      4  1.4744084 0 -0.3503046  1.37829633
-#> 5  1      5  0.2250017 0 -0.5572574  1.32138197
-#> 6  2      1 -2.3242887 1 -0.2440621  0.28554004
+#>   ID period          Y G        X_1          X_2
+#> 1  1      1 -0.8123777 0 -0.4407095 -0.655157012
+#> 2  1      2 -1.8888861 0 -0.2212108 -0.349846262
+#> 3  1      3 -2.2912561 0 -0.9741446 -0.000781637
+#> 4  1      4 -0.5314161 0  0.2259398 -1.557426790
+#> 5  1      5 -1.5528134 0 -0.1413597 -1.590501621
+#> 6  2      1  1.5202663 1 -0.1386675  1.074761245
 ```
 
 ## Testing for Equivalence of Pre-Trends
 
 The `EquiTrends` package contains functions to test for equivalence of
-pre-trends in difference-in-differences estimation. The functions
+pre-trends in Difference-in-Differences estimation. The functions
 `rmsEquivTest`, `meanEquivTest`, and `maxEquivTest` are used to test for
-equivalence of pre-trends in difference-in-differences estimation using
+equivalence of pre-trends in Difference-in-Differences estimation using
 the placebo coefficients $\beta_{l} \ (l=1,...,T)$ estimates. The
 functions are based on the work of Dette & Schumann
 ([2024](https://doi.org/10.1080/07350015.2024.2308121)).
@@ -187,7 +187,7 @@ rmsEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c("X_1", "X_2")
 #> Alternative hypothesis: the root mean squared placebo effect does not exceed the equivalence threshold of 1 .
 #> ---
 #> RMS Placebo Effect   Simulated Crit. Val.    Reject H0 
-#> 0.1812               0.9495                  TRUE      
+#> 0.1835               0.9558                  TRUE      
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -237,7 +237,7 @@ rmsEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c("X_1", "X_2")
 #> Alternative hypothesis: the root mean squared placebo effect does not exceed the equivalence threshold.
 #> ---
 #> RMS Placebo Effect   Min. Equiv. Threshold 
-#> 0.1812               0.3111                
+#> 0.1835               0.2558                
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -365,9 +365,9 @@ maxEquivTest(Y = "Y", ID = "ID", G = "G", period = 2, X= c(5,6),
 #> ( Critical values are printed for the significance level: 0.05 )
 #> ---
 #> Abs. Estimate    Std. Error  Critical Value 
-#> 0.29257          0.1276          0.7902        
-#> 0.07849          0.1276          0.7901        
-#> 0.08192          0.1277          0.7900        
+#> 0.09848          0.1221          0.7992        
+#> 0.27253          0.1221          0.7992        
+#> 0.13041          0.1220          0.7993        
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -396,9 +396,9 @@ maxEquivTest(Y = data_Y, ID = data_ID, G = data_G, period = data_period, X = dat
 #> ( Critical values are printed for the significance level: 0.05 )
 #> ---
 #> Abs. Estimate    Std. Error  Critical Value 
-#> 0.29257          0.1276          0.7902        
-#> 0.07849          0.1276          0.7901        
-#> 0.08192          0.1277          0.7900        
+#> 0.09848          0.1221          0.7992        
+#> 0.27253          0.1221          0.7992        
+#> 0.13041          0.1220          0.7993        
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -422,12 +422,12 @@ maxEquivTest(Y = 3, ID = 1, G = 4, period = 2,
 #> Type: Intersection Union 
 #> Significance level: 0.05 
 #> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold.
-#> Minimum equivalence threshold to accept the alternative: 0.6308 
+#> Minimum equivalence threshold to accept the alternative: 0.4974 
 #> ---
 #>  Estimate    Std. Error   Minimum Equivalence Threshold 
-#> 0.2533       0.2296      0.6308    
-#> 0.2132       0.2267      0.5855    
-#> 0.1569       0.2216      0.5190    
+#> 0.1028       0.2172      0.4489    
+#> 0.1558       0.2088      0.4974    
+#> 0.1257       0.2131      0.4711    
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -537,7 +537,7 @@ maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
 #> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
 #> ---
 #> Max. Abs. Coefficient    Bootstrap Critical Value    Reject H0 
-#> 0.2533                   0.6584                      TRUE      
+#> 0.1558                   0.6586                      TRUE      
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -565,7 +565,7 @@ maxEquivTest(Y = "Y", ID = "ID", G = "G", period = "period",
 #> Alternative hypothesis: the maximum placebo effect does not exceed the equivalence threshold of 1 .
 #> ---
 #> Max. Abs. Coefficient    Bootstrap Critical Value    Reject H0 
-#> 0.2533                   0.6332                      TRUE      
+#> 0.1558                   0.6642                      TRUE      
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -673,7 +673,7 @@ panel before the testing procedure is performed.
 #> Alternative hypothesis: the mean placebo effect does not exceed the equivalence threshold of 1 .
 #> ---
 #> Abs. Mean Placebo Effect Std. Error  p-value Reject H0 
-#> 0.151                    0.1042      <2e-16  TRUE      
+#> 0.1671                   0.09965     <2e-16  TRUE      
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 
@@ -711,7 +711,7 @@ meanEquivTest(Y = "Y", ID = "ID", G = "G", period = "period", X = c(5, 6),
 #> Alternative hypothesis: the mean placebo effect does not exceed the equivalence threshold.
 #> ---
 #> Abs. Mean Placebo Effect Std. Error  Min. Equiv. Threshold 
-#> 0.151                    0.1065      0.3261                
+#> 0.1671                   0.09691     0.3265                
 #> ---
 #> No. placebo coefficients estimated: 3 
 #> Base period: 4 

cran-comments.md

@@ -1,5 +1,10 @@
 # EquiTrends v. 1.0.0
 
+## Resubmission
+This is a resubmission. In this version I have:
+* Removed the space in the doi in the Descrption field of DESCRIPTION.
+* Solved the issue of EquiTrends not being in singles quotes in the DESCRIPTION file by omitting the name from the Description field.
+
 ## R CMD check results
 
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