merge from dev

DESCRIPTION

@@ -14,7 +14,7 @@ Authors@R:
              comment = c(ORCID = "0000-0002-8281-4301")),
       person(given = "Piotr",
              family = "Chlebicki",
-             role = "ctb",
+             role = c("aut", "ctb"),
              email = "[email protected]"))
 Description: Statistical inference with non-probability samples when auxiliary information from external sources such as probability samples or population totals or means is available. Details can be found in: Wu et al. (2020) <doi:10.1080/01621459.2019.1677241>, Kim et al. (2021) <doi:10.1111/rssa.12696>, Wu et al. (2023) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2022002/article/00002-eng.htm>, Kim et al. (2021) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021001/article/00004-eng.htm>, Kim et al. (2020) <doi:10.1111/rssb.12354>.
 License: MIT + file LICENSE

NEWS.md

@@ -1,19 +1,25 @@
 # nonprobsvy 0.1.1
 
--   bugfixes
-    -   bug Fix occuring when estimation was based on auxiliary variable, which led to compression of the data from the frame to the vector.
-    -   bug Fix related to not passing `maxit` argument from `controlSel` function to internally used `nleqslv` function
-    -   bug Fix related to storing `vector` in `model_frame` when predicting `y_hat` in mass imputation `glm` model when X is based in one auxiliary variable only - fix provided converting it to `data.frame` object.
--   features
-    -   add information to `summary` about quality of estimation basing on difference between estimated and known total values of auxiliary variables
-    -   add estimation of exact standard error for k-nearest neighbor estimator.
-    -   add breaking change to `controlOut` function by switching values for `predictive_match` argument. From now on, the `predictive_match = 1` means $\hat{y}-\hat{y}$ in predictive mean matching imputation and `predictive_match = 2` corresponds to $\hat{y}-y$ matching.
-    - implement `div` option when variable selection (more in documentation) for doubly robust estimation.
-    - add more insights to `nonprob` output such as gradient, hessian and jacobian derived from IPW estimation for `mle` and `gee` methods when `IPW` or `DR` model executed.
-    - add estimated inclusion probabilities and its derivatives for probability and non-probability samples to `nonprob` output when `IPW` or `DR` model executed.
-    - add `model_frame` matrix data from probability sample used for mass imputation to `nonprob` when `MI` or `DR` model executed.
-
-## nonprobsvy 0.1.0
+------------------------------------------------------------------------
+
+### Bugfixes
+- bug Fix occuring when estimation was based on auxiliary variable, which led to compression of the data from the frame to the vector.
+- bug Fix related to not passing `maxit` argument from `controlSel` function to internally used `nleqslv` function
+- bug Fix related to storing `vector` in `model_frame` when predicting `y_hat` in mass imputation `glm` model when X is based in one auxiliary variable only - fix provided converting it to `data.frame` object.
+
+### Features
+- add information to `summary` about quality of estimation basing on difference between estimated and known total values of auxiliary variables
+- add estimation of exact standard error for k-nearest neighbor estimator.
+- add breaking change to `controlOut` function by switching values for `predictive_match` argument. From now on, the `predictive_match = 1` means $\hat{y}-\hat{y}$ in predictive mean matching imputation and `predictive_match = 2` corresponds to $\hat{y}-y$ matching.
+- implement `div` option when variable selection (more in documentation) for doubly robust estimation.
+- add more insights to `nonprob` output such as gradient, hessian and jacobian derived from IPW estimation for `mle` and `gee` methods when `IPW` or `DR` model executed.
+- add estimated inclusion probabilities and its derivatives for probability and non-probability samples to `nonprob` output when `IPW` or `DR` model executed.
+- add `model_frame` matrix data from probability sample used for mass imputation to `nonprob` when `MI` or `DR` model executed.
+
+### Unit tests
+- added unit tests for variable selection models and mi estimation with vector of population totals available
+
+# nonprobsvy 0.1.0
 
 ------------------------------------------------------------------------
 

README.Rmd

@@ -469,6 +469,6 @@ summary(result_ipw)
 ## Funding
 
 Work on this package is supported by the National Science Centre, OPUS
-22 grant no. 2020/39/B/HS4/00941.
+20 grant no. 2020/39/B/HS4/00941.
 
 ## References (selected)

README.md

@@ -631,7 +631,7 @@ summary(result_ipw)
 ## Funding
 
 Work on this package is supported by the National Science Centre, OPUS
-22 grant no. 2020/39/B/HS4/00941.
+20 grant no. 2020/39/B/HS4/00941.
 
 ## References (selected)