Merge pull request #38 from ncn-foreigners/dev

Dev

.gitignore

@@ -10,4 +10,5 @@ playground
 src/*.o
 src/*.so
 src/Makevars
+srs/*.dll
 docs

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: nonprobsvy
 Type: Package
-Title: Package for Inference Based on Nonprobability Samples
+Title: Package for Inference Based on Non-Probability Samples
 Version: 0.1.0
 Authors@R:
     c(person(given = "Łukasz",
@@ -16,12 +16,7 @@ Authors@R:
              family = "Chlebicki",
              role = "ctb",
              email = "[email protected]"))
-Description: An R package for 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>.
+Description: An R package for 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
 Encoding: UTF-8
 LazyData: true
@@ -35,7 +30,8 @@ Suggests:
     covr,
     sampling,
     spelling
-Depends: survey
+Depends:
+  survey
 Imports: 
     maxLik,
     stats,
@@ -44,12 +40,13 @@ Imports:
     ncvreg,
     mathjaxr,
     RANN,
-    Rcpp,
+    Rcpp (>= 1.0.12),
     nleqslv,
-    doParallel,
+    doSNOW,
+    progress,
     foreach,
     parallel
 LinkingTo: 
-    RcppArmadillo,
-    Rcpp
+    Rcpp,
+    RcppArmadillo
 Language: en-US

NAMESPACE

@@ -30,7 +30,8 @@ importFrom(Matrix,Diagonal)
 importFrom(Matrix,Matrix)
 importFrom(RANN,nn2)
 importFrom(Rcpp,evalCpp)
-importFrom(doParallel,registerDoParallel)
+importFrom(Rcpp,sourceCpp)
+importFrom(doSNOW,registerDoSNOW)
 importFrom(foreach,"%dopar%")
 importFrom(foreach,foreach)
 importFrom(maxLik,maxLik)
@@ -47,19 +48,23 @@ importFrom(stats,confint)
 importFrom(stats,contrasts)
 importFrom(stats,cooks.distance)
 importFrom(stats,cor)
+importFrom(stats,cov)
 importFrom(stats,delete.response)
 importFrom(stats,deviance)
 importFrom(stats,dnorm)
 importFrom(stats,get_all_vars)
 importFrom(stats,glm.fit)
 importFrom(stats,hatvalues)
 importFrom(stats,lm.fit)
+importFrom(stats,loess)
+importFrom(stats,loess.control)
 importFrom(stats,logLik)
 importFrom(stats,model.frame)
 importFrom(stats,model.matrix)
 importFrom(stats,model.response)
 importFrom(stats,nobs)
 importFrom(stats,pnorm)
+importFrom(stats,predict)
 importFrom(stats,predict.glm)
 importFrom(stats,printCoefmat)
 importFrom(stats,pt)
@@ -75,8 +80,13 @@ importFrom(stats,summary.glm)
 importFrom(stats,terms)
 importFrom(stats,uniroot)
 importFrom(stats,update)
+importFrom(stats,var)
 importFrom(stats,vcov)
 importFrom(stats,weighted.mean)
 importFrom(survey,as.svrepdesign)
+importFrom(survey,svymean)
 importFrom(survey,svyrecvar)
+importFrom(utils,setTxtProgressBar)
+importFrom(utils,txtProgressBar)
 useDynLib(nonprobsvy)
+useDynLib(nonprobsvy, .registration = TRUE)

NEWS.md

@@ -6,7 +6,7 @@
 
 - implemented population mean estimation using doubly robust, inverse probability weighting and mass imputation methods
 - implemented inverse probability weighting models with Maximum Likelihood Estimation and Generalized Estimating Equations methods with `logit`, `complementary log-log` and `probit` link functions.
-- implemented `generalized linear models` and `nearest neighbours` methods for Mass Imputation
+- implemented `generalized linear models`, `nearest neighbours` and `predictive mean matching` methods for Mass Imputation
 - implemented estimation methods when vector of population means/totals is available
 - implemented variables selection with `SCAD`, `LASSO` and `MCP` penalization equations
 - implemented `analytic` and `bootstrap` (with parallel computation) variance for described estimators