## 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 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.

### Github repository

- added automated
`R-cmd`

check

### Documentation

- added documentation for
`nonprob`

function.