update according to CRAN

LICENSE

@@ -1,21 +1,2 @@
-MIT License
-
-Copyright (c) 2018 Benjamin Elbers
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+YEAR: 2018
+COPYRIGHT HOLDER: Benjamin Elbers

README.Rmd

@@ -25,7 +25,7 @@ on the Mutual Information Index (M).
 - calculate total, between, within, and local segregation
 - decompose differences in total segregation over time
 - estimate standard errors via bootstrapping
-- every method returns a [tidy](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) data frame (or [tibble](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html), if the package is installed) for easy post-processing and plotting
+- every method returns a [tidy](http://vita.had.co.nz/papers/tidy-data.html) data frame (or [tibble](http://tibble.tidyverse.org), if the package is installed) for easy post-processing and plotting
 - it's fast, because it uses the [`data.table`](https://github.com/Rdatatable/data.table/wiki) package internally
 
 ## Example

README.md

@@ -2,18 +2,25 @@
 segregation
 ===========
 
-An R package to calculate entropy-based segregation indices, with a focus on the Mutual Information Index (M).
+An R package to calculate entropy-based segregation indices, with a
+focus on the Mutual Information Index (M).
 
 -   calculate total, between, within, and local segregation
 -   decompose differences in total segregation over time
 -   estimate standard errors via bootstrapping
--   every method returns a [tidy](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) data frame (or [tibble](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html), if the package is installed) for easy post-processing and plotting
--   it's fast, because it uses the [`data.table`](https://github.com/Rdatatable/data.table/wiki) package internally
+-   every method returns a
+    [tidy](http://vita.had.co.nz/papers/tidy-data.html) data frame (or
+    [tibble](http://tibble.tidyverse.org), if the package is installed)
+    for easy post-processing and plotting
+-   it’s fast, because it uses the
+    [`data.table`](https://github.com/Rdatatable/data.table/wiki)
+    package internally
 
 Example
 -------
 
-The package provides an easy way to calculate total and local segregation, based on the Mutual Information Index.
+The package provides an easy way to calculate total and local
+segregation, based on the Mutual Information Index.
 
 ``` r
 library(segregation)
@@ -22,7 +29,7 @@ mutual_total(schools00, "school", "race", weight = "n")
 #> # A tibble: 3 x 2
 #>   stat    est
 #> * <chr> <dbl>
-#> 1 M     0.425
+#> 1 M     0.426
 #> 2 M_min 0.   
 #> 3 M_max 1.61
 ```
@@ -35,50 +42,52 @@ mutual_total(schools00, "school", "race", weight = "n", se = TRUE)
 #> # A tibble: 3 x 3
 #>   stat    est       se
 #> * <chr> <dbl>    <dbl>
-#> 1 M     0.429 0.000851
+#> 1 M     0.429 0.000935
 #> 2 M_min 0.    0.      
 #> 3 M_max 1.61  0.
 ```
 
-Local segregation (`ls`) of racial groups, with group-specific standard errors:
+Local segregation (`ls`) of racial groups, with group-specific standard
+errors:
 
 ``` r
 mutual_local(schools00, "school", "race", weight = "n", se = TRUE)
 #> ..........
 #> # A tibble: 15 x 4
-#>    race   stat        est        se
-#>    <fct>  <fct>     <dbl>     <dbl>
-#>  1 native ls      1.53    0.0134   
-#>  2 asian  ls      0.663   0.00302  
-#>  3 hisp   ls      0.781   0.00141  
-#>  4 black  ls      0.889   0.00251  
-#>  5 white  ls      0.184   0.000806 
-#>  6 native p       0.00760 0.0000859
-#>  7 asian  p       0.0227  0.000136 
-#>  8 hisp   p       0.152   0.000426 
-#>  9 black  p       0.189   0.000254 
-#> 10 white  p       0.629   0.000584 
-#> 11 native M_group 0.0116  0.000162 
-#> 12 asian  M_group 0.0151  0.000134 
-#> 13 hisp   M_group 0.119   0.000391 
-#> 14 black  M_group 0.168   0.000502 
-#> 15 white  M_group 0.115   0.000427
+#>    race   stat        est       se
+#>    <fct>  <fct>     <dbl>    <dbl>
+#>  1 asian  ls      0.667   0.00674 
+#>  2 black  ls      0.885   0.00259 
+#>  3 hisp   ls      0.782   0.00258 
+#>  4 white  ls      0.184   0.000725
+#>  5 native ls      1.53    0.0229  
+#>  6 asian  p       0.0226  0.000124
+#>  7 black  p       0.190   0.000465
+#>  8 hisp   p       0.152   0.000317
+#>  9 white  p       0.628   0.000687
+#> 10 native p       0.00745 0.000135
+#> 11 asian  M_group 0.0151  0.000193
+#> 12 black  M_group 0.168   0.000354
+#> 13 hisp   M_group 0.119   0.000336
+#> 14 white  M_group 0.116   0.000357
+#> 15 native M_group 0.0114  0.000101
 ```
 
-Decompose the difference in segregation between 2000 and 2005, using the method developed by Mora and Ruiz-Castillo (2009):
+Decompose the difference in segregation between 2000 and 2005, using the
+method developed by Mora and Ruiz-Castillo (2009):
 
 ``` r
 mutual_difference(schools00, schools05, "school", "race", 
                   weight = "n", method = "mrc")
 #> # A tibble: 6 x 2
 #>   stat                est
 #> * <chr>             <dbl>
-#> 1 M1              0.425  
-#> 2 M2              0.415  
-#> 3 diff           -0.0102 
-#> 4 group_marginal  0.00768
-#> 5 unit_entropy   -0.0639 
-#> 6 invariant       0.0460
+#> 1 M1              0.426  
+#> 2 M2              0.413  
+#> 3 diff           -0.0122 
+#> 4 group_marginal  0.00747
+#> 5 unit_entropy   -0.0641 
+#> 6 invariant       0.0445
 ```
 
 How to install
@@ -103,18 +112,36 @@ Papers using the Mutual information index
 
 (list incomplete)
 
--   DiPrete, T. A., Eller, C. C., Bol, T., & van de Werfhorst, H. G. (2017). School-to-Work Linkages in the United States, Germany, and France. American Journal of Sociology, 122(6), 1869-1938. <https://doi.org/10.1086/691327>
--   Forster, A. G., & Bol, T. (2017). Vocational education and employment over the life course using a new measure of occupational specificity. Social Science Research, 70, 176-197. <https://doi.org/10.1016/j.ssresearch.2017.11.004>
--   Van Puyenbroeck, T., De Bruyne, K., & Sels, L. (2012). More than ‘Mutual Information’: Educational and sectoral gender segregation and their interaction on the Flemish labor market. Labour Economics, 19(1), 1-8. <https://doi.org/10.1016/j.labeco.2011.05.002>
--   Mora, R., & Ruiz-Castillo, J. (2003). Additively decomposable segregation indexes. The case of gender segregation by occupations and human capital levels in Spain. The Journal of Economic Inequality, 1(2), 147-179. <https://doi.org/10.1023/A:1026198429377>
+-   DiPrete, T. A., Eller, C. C., Bol, T., & van de Werfhorst, H. G.
+    (2017). School-to-Work Linkages in the United States, Germany, and
+    France. American Journal of Sociology, 122(6), 1869-1938.
+    <https://doi.org/10.1086/691327>
+-   Forster, A. G., & Bol, T. (2017). Vocational education and
+    employment over the life course using a new measure of occupational
+    specificity. Social Science Research, 70, 176-197.
+    <https://doi.org/10.1016/j.ssresearch.2017.11.004>
+-   Van Puyenbroeck, T., De Bruyne, K., & Sels, L. (2012). More than
+    ‘Mutual Information’: Educational and sectoral gender segregation
+    and their interaction on the Flemish labor market. Labour Economics,
+    19(1), 1-8. <https://doi.org/10.1016/j.labeco.2011.05.002>
+-   Mora, R., & Ruiz-Castillo, J. (2003). Additively decomposable
+    segregation indexes. The case of gender segregation by occupations
+    and human capital levels in Spain. The Journal of Economic
+    Inequality, 1(2), 147-179. <https://doi.org/10.1023/A:1026198429377>
 
 References on entropy-based segregation indices
 -----------------------------------------------
 
 Theil, Henri. (1971). Principles of Econometrics. New York: Wiley.
 
-Frankel, D. M., & Volij, O. (2011). Measuring school segregation. Journal of Economic Theory, 146(1), 1-38. <https://doi.org/10.1016/j.jet.2010.10.008>
+Frankel, D. M., & Volij, O. (2011). Measuring school segregation.
+Journal of Economic Theory, 146(1), 1-38.
+<https://doi.org/10.1016/j.jet.2010.10.008>
 
-Mora, R., & Ruiz-Castillo, J. (2009). The Invariance Properties of the Mutual Information Index of Multigroup Segregation. Research on Economic Inequality, 17, 33-53.
+Mora, R., & Ruiz-Castillo, J. (2009). The Invariance Properties of the
+Mutual Information Index of Multigroup Segregation. Research on Economic
+Inequality, 17, 33-53.
 
-Mora, R., & Ruiz-Castillo, J. (2011). Entropy-based Segregation Indices. Sociological Methodology, 41(1), 159–194. <https://doi.org/10.1111/j.1467-9531.2011.01237.x>
+Mora, R., & Ruiz-Castillo, J. (2011). Entropy-based Segregation Indices.
+Sociological Methodology, 41(1), 159–194.
+<https://doi.org/10.1111/j.1467-9531.2011.01237.x>

cran-comments.md

@@ -1,4 +1,7 @@
-This is the first submission of this package.
+This is a resubmission.
+
+* fixed LICENSE file
+* updated URLs in README
 
 ## Test environments