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-## version 0.0.1
+## version 0.1.0
 
 ------------------------------------------------------------------------
 
 ### Features
 
-- 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 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
-- added control parameters for models
+-   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 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 - `doSNOW` 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 IPW estimators.
 
 ### Github repository
 
-- added automated `R-cmd` check
+-   added automated `R-cmd` check
 
 ### Documentation
 
-- added documentation for `nonprob` function.
+-   added documentation for `nonprob` function.