Updating of academicImpactSummary

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: cfaTools
-Version: 0.0-1.99
-Date: 2021-10-17
+Version: 0.0-1.991
+Date: 2021-10-19
 Title: Center for Assessment Tools for Large Scale Educational Assessment Analysis
 Description: Center for Assessment developed Tools for Large Scale Educational Assessment.
 Authors@R: c(person(given=c("Damian", "W."), family="Betebenner", email="[email protected]", role=c("aut", "cre")),

R/academicImpactSummary.R

@@ -7,7 +7,8 @@
  all_grades=NULL,
  sgp_grades=NULL,
  aggregation_group="SCHOOL_NUMBER",
- years_for_aggregates=NULL) {
+ years_for_aggregates=NULL,
+ rtm_adjustment=TRUE) {
 
  ACHIEVEMENT_LEVEL <- ACHIEVEMENT_LEVEL_PRIOR_2YEAR <- CONTENT_AREA <- COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP <- NULL
  COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP_ADJ <- COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS <- COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS_ADJ <- NULL
@@ -24,6 +25,7 @@
  state <- SGP::getStateAbbreviation(tmp.name)
  }
 
+
  ### Create sgp_data data set
  if (SGP::is.SGP(sgp_data)) sgp_data <- sgp_data@Data
  if (!"data.table" %in% class(sgp_data)) stop("Please Provide either and SGP object or LONG data")
@@ -109,16 +111,16 @@
  setkeyv(group_aggregates, shift.key)
 
  group_aggregates[, c("MEDIAN_SGP_PRIOR_1YEAR", "MEDIAN_SGP_PRIOR_2YEAR") := shift(MEDIAN_SGP, 1:2), by = c(eval(aggregation_group), "CONTENT_AREA")]
- group_aggregates[, MEDIAN_SGP_BASELINE_PRIOR := shift(MEDIAN_SGP_BASELINE, 1), by = c(eval(aggregation_group), "CONTENT_AREA")] # Only getting this for 2021 (1 year shift = 2 years)
+ group_aggregates[, MEDIAN_SGP_BASELINE_PRIOR_2YEAR := shift(MEDIAN_SGP_BASELINE, 1), by = c(eval(aggregation_group), "CONTENT_AREA")] # Only getting this for 2021 (1 year shift = 2 years)
  group_aggregates[, c("MEAN_SCALE_SCORE_PRIOR_STANDARDIZED", "MEAN_SCALE_SCORE_PRIOR_2YEAR_STANDARDIZED") := shift(MEAN_SCALE_SCORE_STANDARDIZED, 1:2), by = c(eval(aggregation_group), "CONTENT_AREA")]
  group_aggregates[, c("MEAN_SCALE_SCORE_PRIOR_PERCENTILE", "MEAN_SCALE_SCORE_PRIOR_2YEAR_PERCENTILE") := shift(MEAN_SCALE_SCORE_PERCENTILE, 1:2), by = c(eval(aggregation_group), "CONTENT_AREA")]
  group_aggregates[, c("PERCENT_PROFICIENT_PRIOR", "PERCENT_PROFICIENT_PRIOR_2YEAR") := shift(PERCENT_PROFICIENT, 1:2), by = c(eval(aggregation_group), "CONTENT_AREA")]
 
  # table(group_aggregates[, YEAR, is.na(MEDIAN_SGP_PRIOR_2YEAR)])
- # table(group_aggregates[, YEAR, is.na(MEDIAN_SGP_BASELINE_PRIOR)])
+ # table(group_aggregates[, YEAR, is.na(MEDIAN_SGP_BASELINE_PRIOR_2YEAR)])
 
  if (year_gap!=1) {
- group_aggregates[YEAR == current_year, MEDIAN_SGP_PRIOR_2YEAR := MEDIAN_SGP_BASELINE_PRIOR] # Using only BASELINE to BASELINE for 2021
+ # group_aggregates[YEAR == current_year, MEDIAN_SGP_PRIOR_2YEAR := MEDIAN_SGP_BASELINE_PRIOR_2YEAR] # Using only BASELINE to BASELINE for 2021
  group_aggregates[YEAR == current_year, MEDIAN_SGP_PRIOR_2YEAR := MEDIAN_SGP_PRIOR_1YEAR]
  group_aggregates[YEAR == current_year, MEDIAN_SGP_PRIOR_1YEAR := NA]
  group_aggregates[YEAR == current_year, MEAN_SCALE_SCORE_PRIOR_2YEAR_STANDARDIZED := MEAN_SCALE_SCORE_PRIOR_STANDARDIZED]
@@ -144,12 +146,17 @@
  ### Create uncorrected Baseline difference (2021 - 2019)
  group_aggregates[, MSGP_BASELINE_DIFFERENCE_UNCORRECTED := MEDIAN_SGP_BASELINE - MEDIAN_SGP_PRIOR_2YEAR]
 
+ if (rtm_adjustment) {
  ### RTM Adjusted MSGP_BASELINE_DIFFERENCE
  msgp_rtm_models <- list()
+
  for (CA in content_areas) {
- msgp_rtm_models[[CA]] <- MASS::rlm(MSGP_BASELINE_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSGP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ msgp_rtm_models[[CA]] <- MASS::rlm(MSGP_BASELINE_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSGP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ msgp_rtm_models[[CA]][["coef"]][["PRIOR_MSGP_CENTERED_2YEAR"]] <- 0
+ }
  }
-
  ## Model diagnostics
  # par(mfrow = c(2, 2))
  # for (CA in content_areas) {
@@ -164,6 +171,7 @@
  for (CA in content_areas) {
  group_aggregates[CONTENT_AREA == CA, MSGP_BASELINE_DIFFERENCE_ADJUSTED := MSGP_BASELINE_DIFFERENCE_UNCORRECTED - (PRIOR_MSGP_CENTERED_2YEAR*msgp_rtm_models[[CA]]$coef[["PRIOR_MSGP_CENTERED_2YEAR"]])]
  }
+ } # END rtm_adjustment
 
  ## correlation checks
  # cor(group_aggregates[, MSGP_BASELINE_DIFFERENCE_UNCORRECTED, MEDIAN_SGP_PRIOR_2YEAR], use='complete.obs')
@@ -186,6 +194,7 @@
  group_aggregates[, COVID_ACADEMIC_IMPACT_SGP_DIFF :=
  factor(COVID_ACADEMIC_IMPACT_SGP_DIFF, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
 
+ if (rtm_adjustment) {
  group_aggregates[, COVID_ACADEMIC_IMPACT_SGP_DIFF_ADJ := fcase(
  MSGP_BASELINE_DIFFERENCE_ADJUSTED >= 5, "Improvement",
  MSGP_BASELINE_DIFFERENCE_ADJUSTED < 5 & MSGP_BASELINE_DIFFERENCE_ADJUSTED >= -5, "Modest to None",
@@ -195,7 +204,7 @@
 
  group_aggregates[, COVID_ACADEMIC_IMPACT_SGP_DIFF_ADJ :=
  factor(COVID_ACADEMIC_IMPACT_SGP_DIFF_ADJ, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
-
+ }
  # table(group_aggregates[YEAR==prior_year, COVID_ACADEMIC_IMPACT_SGP_DIFF, CONTENT_AREA], exclude=NULL)
  # table(group_aggregates[YEAR==current_year, COVID_ACADEMIC_IMPACT_SGP_DIFF, CONTENT_AREA], exclude=NULL)
  # table(group_aggregates[YEAR==prior_year, COVID_ACADEMIC_IMPACT_SGP_DIFF_ADJ, CONTENT_AREA], exclude=NULL)
@@ -226,12 +235,16 @@
  ## Merge in GES with other summary statistics
  group_aggregates <- ges_sgp[group_aggregates]
 
+ if (rtm_adjustment) {
  ### RTM Adjusted G.E.S.
  gessgp_rtm_models <- list()
  for (CA in content_areas) {
- gessgp_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SGP ~ 0 + PRIOR_MSGP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ gessgp_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SGP ~ 0 + PRIOR_MSGP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ gessgp_rtm_models[[CA]][["coef"]][["PRIOR_MSGP_CENTERED_2YEAR"]] <- 0
+ }
  }
-
  ## Model diagnostics
  # par(mfrow = c(2, 2))
  # for (CA in content_areas) {
@@ -246,6 +259,7 @@
  for (CA in content_areas) {
  group_aggregates[CONTENT_AREA == CA, GES_MEDIAN_SGP_ADJUSTED := GES_MEDIAN_SGP - (PRIOR_MSGP_CENTERED_2YEAR*gessgp_rtm_models[[CA]]$coef[["PRIOR_MSGP_CENTERED_2YEAR"]])]
  }
+ } # END rtm_adjustment
 
  ## Visualization, summary and correlation checks
  # na.omit(group_aggregates[, as.list(round(summary(GES_MEDIAN_SGP_ADJUSTED),3)), keyby=c("YEAR", "CONTENT_AREA")])
@@ -280,6 +294,7 @@
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP :=
  factor(COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
 
+ if (rtm_adjustment) {
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP_ADJ := fcase(
  GES_MEDIAN_SGP_ADJUSTED >= 0.2, "Improvement",
  GES_MEDIAN_SGP_ADJUSTED < 0.2 & GES_MEDIAN_SGP_ADJUSTED >= -0.2, "Modest to None",
@@ -289,7 +304,7 @@
 
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP_ADJ :=
  factor(COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP_ADJ, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
-
+ }
  # table(group_aggregates[YEAR==prior_year, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP, CONTENT_AREA], exclude=NULL)
  # table(group_aggregates[YEAR==current_year, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP, CONTENT_AREA], exclude=NULL)
  # table(group_aggregates[YEAR==prior_year, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SGP_ADJ, CONTENT_AREA], exclude=NULL)
@@ -305,13 +320,23 @@
  group_aggregates[, MSSS_DIFFERENCE_UNCORRECTED := MEAN_SCALE_SCORE_STANDARDIZED - MEAN_SCALE_SCORE_PRIOR_2YEAR_STANDARDIZED]
  group_aggregates[, MSSP_DIFFERENCE_UNCORRECTED := MEAN_SCALE_SCORE_PERCENTILE - MEAN_SCALE_SCORE_PRIOR_2YEAR_PERCENTILE]
 
+ if (rtm_adjustment) {
  ### RTM Adjusted MSSS_DIFFERENCE
  msss_rtm_models <- mssp_rtm_models <- list()
+
  for (CA in content_areas) {
- msss_rtm_models[[CA]] <- MASS::rlm(MSSS_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSSS_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ msss_rtm_models[[CA]] <- MASS::rlm(MSSS_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSSS_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ msss_rtm_models[[CA]][["coef"]][["PRIOR_MSSS_CENTERED_2YEAR"]] <- 0
+ }
  }
  for (CA in content_areas) {
- mssp_rtm_models[[CA]] <- MASS::rlm(MSSP_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSSP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ mssp_rtm_models[[CA]] <- MASS::rlm(MSSP_DIFFERENCE_UNCORRECTED ~ 0 + PRIOR_MSSP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ mssp_rtm_models[[CA]][["coef"]][["PRIOR_MSSP_CENTERED_2YEAR"]] <- 0
+ }
  }
 
  ## Model diagnostics
@@ -331,7 +356,7 @@
  for (CA in content_areas) {
  group_aggregates[CONTENT_AREA == CA, MSSP_DIFFERENCE_ADJUSTED := MSSP_DIFFERENCE_UNCORRECTED - (PRIOR_MSSP_CENTERED_2YEAR*mssp_rtm_models[[CA]]$coef[["PRIOR_MSSP_CENTERED_2YEAR"]])]
  }
-
+ } # END rtm_adjustment
  ## correlation checks
  # cor(group_aggregates[, MSSS_DIFFERENCE_UNCORRECTED, MEAN_SCALE_SCORE_PRIOR_STANDARDIZED], use='complete.obs')
  # cor(group_aggregates[YEAR == prior_year, MSSS_DIFFERENCE_UNCORRECTED, MEAN_SCALE_SCORE_PRIOR_STANDARDIZED], use='complete.obs')
@@ -363,10 +388,15 @@
  ## Merge in GES with other summary statistics
  group_aggregates <- ges_sss[group_aggregates]
 
+ if (rtm_adjustment) {
  ### RTM Adjusted G.E.S.
  gesss_rtm_models <- list()
  for (CA in content_areas) {
- gesss_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SSS ~ 0 + PRIOR_MSSS_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ gesss_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SSS ~ 0 + PRIOR_MSSS_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ gesss_rtm_models[[CA]][["coef"]][["PRIOR_MSSS_CENTERED_2YEAR"]] <- 0
+ }
  }
 
  ## Model diagnostics
@@ -383,7 +413,7 @@
  for (CA in content_areas) {
  group_aggregates[CONTENT_AREA == CA, GES_MEDIAN_SSS_ADJUSTED := GES_MEDIAN_SSS - (PRIOR_MSSS_CENTERED_2YEAR*gesss_rtm_models[[CA]]$coef[["PRIOR_MSSS_CENTERED_2YEAR"]])]
  }
-
+ } # END rtm_adjustment
 
  #####
  ### Gamma Effect Size (within aggregation_group MSSP 2021 - MSSP 2019)
@@ -405,10 +435,16 @@
  ## Merge in GES with other summary statistics
  group_aggregates <- ges_ssp[group_aggregates]
 
+ if (rtm_adjustment) {
  ### RTM Adjusted G.E.S.
  gessp_rtm_models <- list()
+
  for (CA in content_areas) {
- gessp_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SSP ~ 0 + PRIOR_MSSP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ if (!is.null(aggregation_group)) {
+ gessp_rtm_models[[CA]] <- MASS::rlm(GES_MEDIAN_SSP ~ 0 + PRIOR_MSSP_CENTERED_2YEAR, data=group_aggregates[YEAR == prior_year & CONTENT_AREA == CA])
+ } else {
+ gessp_rtm_models[[CA]][["coef"]][["PRIOR_MSSP_CENTERED_2YEAR"]] <- 0
+ }
  }
 
  ## Model diagnostics
@@ -425,7 +461,7 @@
  for (CA in content_areas) {
  group_aggregates[CONTENT_AREA == CA, GES_MEDIAN_SSP_ADJUSTED := GES_MEDIAN_SSP - (PRIOR_MSSP_CENTERED_2YEAR*gessp_rtm_models[[CA]]$coef[["PRIOR_MSSP_CENTERED_2YEAR"]])]
  }
-
+ } # END rtm_adjustment
 
  ## Visualization, summary and correlation checks
  # na.omit(group_aggregates[, as.list(round(summary(GES_MEDIAN_SSS_ADJUSTED),3)), keyby=c("YEAR", "CONTENT_AREA")])
@@ -471,6 +507,7 @@
  group_aggregates[, COVID_ACADEMIC_IMPACT_SSP_DIFF :=
  factor(COVID_ACADEMIC_IMPACT_SSP_DIFF, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
 
+ if (rtm_adjustment) {
  group_aggregates[, COVID_ACADEMIC_IMPACT_SSS_DIFF_ADJ := fcase(
  MSSS_DIFFERENCE_ADJUSTED >= 5, "Improvement",
  MSSS_DIFFERENCE_ADJUSTED < 5 & MSSS_DIFFERENCE_ADJUSTED >= -5, "Modest to None",
@@ -490,6 +527,7 @@
 
  group_aggregates[, COVID_ACADEMIC_IMPACT_SSP_DIFF_ADJ :=
  factor(COVID_ACADEMIC_IMPACT_SSP_DIFF_ADJ, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
+ }
 
  ## Create COVID Impact Levels for G.E.S. for 2021 - 2019 Median SGP differences
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS := fcase(
@@ -512,6 +550,7 @@
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS :=
  factor(COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS, levels=c("Improvement", "Modest to None", "Moderate", "Large", "Severe"), ordered=TRUE)]
 
+ if (rtm_adjustment) {
  group_aggregates[, COVID_ACADEMIC_IMPACT_GES_MEDIAN_SSS_ADJ := fcase(
  GES_MEDIAN_SSS_ADJUSTED >= 0.2, "Improvement",
  GES_MEDIAN_SSS_ADJUSTED < 0.2 & GES_MEDIAN_SSS_ADJUSTED >= -0.2, "Modest to None",
@@ -539,12 +578,10 @@
  setattr(group_aggregates, "gesss_rtm_models", lapply(gesss_rtm_models, summary))
  setattr(group_aggregates, "mssp_rtm_models", lapply(mssp_rtm_models, summary))
  setattr(group_aggregates, "gessp_rtm_models", lapply(gessp_rtm_models, summary))
+ }
 
  tmp.list <- list(TEMP=group_aggregates)
- if (is.null(aggregation_group)) {
- names(tmp.list) <- "STATE"
- } else {
- names(tmp.list) <- paste(aggregation_group, collapse="_by_")
- }
+ if (is.null(aggregation_group)) aggregation_group <- "CONTENT_AREA"
+ names(tmp.list) <- paste(aggregation_group, collapse="_by_")
  return(tmp.list)
 } ### END academicImpactSummary

R/academicImpactSummary_Output.R

@@ -0,0 +1,6 @@
+`academicImpactSummary_Output` <- function(
+ academicImpactSummary_Data,
+ state=NULL,
+ output_year=NULL) {
+
+} ### END academicImpactSummary_Output

R/zzz.R

@@ -5,6 +5,6 @@ function(libname, pkgname) {
 `.onAttach` <-
 function(libname, pkgname) {
  if (interactive()) {
- packageStartupMessage(magenta$bold('cfaTools',paste(paste0(unlist(strsplit(as.character(packageVersion("cfaTools")), "[.]")), c(".", "-", ".", "")), collapse=""),' (10-17-2021). For help: >help("cfaTools") or visit https://centerforassessment.github.io/cfaTools'))
+ packageStartupMessage(magenta$bold('cfaTools',paste(paste0(unlist(strsplit(as.character(packageVersion("cfaTools")), "[.]")), c(".", "-", ".", "")), collapse=""),' (10-19-2021). For help: >help("cfaTools") or visit https://centerforassessment.github.io/cfaTools'))
  }
 }

README.md

@@ -4,7 +4,7 @@ cfaTools
 [![R-CMD-check](https://github.com/CenterForAssessment/cfaTools/workflows/R-CMD-check/badge.svg)](https://github.com/CenterForAssessment/cfaTools/actions)
 [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/centerforassessment/cfaTools?branch=master&svg=true)](https://ci.appveyor.com/project/centerforassessment/cfaTools)
 [![CRAN_Status_Badge](http:https://www.r-pkg.org/badges/version/cfaTools)](http:https://cran.r-project.org/package=cfaTools)
-[![Development Version](https://img.shields.io/badge/devel-0.0--1.99-brightgreen.svg)](https://github.com/CenterForAssessment/cfaTools)
+[![Development Version](https://img.shields.io/badge/devel-0.0--1.991-brightgreen.svg)](https://github.com/CenterForAssessment/cfaTools)
 [![License](http:https://img.shields.io/badge/license-GPL%203-brightgreen.svg?style=flat)](https://github.com/CenterForAssessment/cfaTools/blob/master/LICENSE.md)
 
 
@@ -27,7 +27,7 @@ To install the latest stable release of **cfaTools** from [CRAN](https://CRAN.R-
 To install the development release of **cfaTools** from [GitHub](https://github.com/CenterForAssessment/cfaTools/):
 
 ```R
-> devtools::install_github("CenterForAssessment/cfaTools")
+> remotes::install_github("centerforassessment/cfaTools")
 ```
 
 

man/academicImpactSummary.Rd

@@ -13,7 +13,8 @@ academicImpactSummary(
  all_grades=NULL,
  sgp_grades=NULL,
  aggregation_group="SCHOOL_NUMBER",
- years_for_aggregates=NULL)
+ years_for_aggregates=NULL,
+ rtm_adjustment=TRUE)
 }
 
 \arguments{
@@ -33,7 +34,9 @@ academicImpactSummary(
  }
  \item{aggregation_group}{Character string indicating groups to aggregate by and create impact summaries for.
  }
- \item{years_for_aggregates}{One or both of prior_year and current_year to create aggregates for. Defaults is NULL which gets turned into both prior_year and current_year.
+ \item{years_for_aggregates}{One or both of prior_year and current_year to create aggregates for. Defaults is NULL which gets turned into both prior_year and current_year.
+ }
+ \item{rtm_adjustment}{Logical. Should the regression to the mean adjustments be calculated.
  }
 }
 

man/cfaTools-package.Rd

@@ -12,8 +12,8 @@ Center for Assessment developed Tools for Large Scale Educational Assessment.
 \tabular{ll}{
 Package: \tab cfaTools\cr
 Type: \tab Package\cr
-Version: \tab 0.0-1.99\cr
-Date: \tab 2021-10-17\cr
+Version: \tab 0.0-1.991\cr
+Date: \tab 2021-10-19\cr
 License: \tab GPL-3\cr
 LazyLoad: \tab yes\cr
 }