nonprobsvy version 0.1.0

Version of the package submitted to CRAN

nonprobsvy 0.1.0

• 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, nearest neighbours and predictive mean matching methods for Mass Imputation
• implemented y-yhat and yhat-yhat predictive mean matching
• implemented bias correction estimators for doubly-robust approach
• 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 - doParallel package) variance for described estimators
• added control parameters for models
• added S3 methods for object of nonprob class such as
  • nobs for samples size
  • pop.size for population size estimation
  • residuals for residuals of the inverse probability weighting model
  • cooks.distance for identifying influential observations that have a significant impact on the parameter estimates
  • hatvalues for measuring the leverage of individual observations
  • logLik for computing the log-likelihood of the model,
  • AIC (Akaike Information Criterion) for evaluating the model based on the trade-off between goodness of fit and complexity, helping in model selection
  • BIC (Bayesian Information Criterion) for a similar purpose as AIC but with a stronger penalty for model complexity
  • confint for calculating confidence intervals around parameter estimates
  • vcov for obtaining the variance-covariance matrix of the parameter estimates
  • deviance for assessing the goodness of fit of the model

Unit tests

• added unit tests for IPW estimators
• added unit tests for MI estimators
• added unit tests for DR estimators
• added unit tests for variable selection models
• Multicore tests will only be performed after TEST_NONPROBSVY_MULTICORE_DEVELOPER
  is set to "true" via Sys.setenv

Github repository

• added automated R-cmd check
• added CRAN and codecov badges

Documentation

• added documentation for nonprob function
• added documenation for control functions
• added documentation for link functions

Full changelog: v0.1.0