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NEWS.md update
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| ## version 0.0.1 | ||
| ## version 0.1.0 | ||
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| ------------------------------------------------------------------------ | ||
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| ### Features | ||
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| - 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 | ||
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| ### Unit tests | ||
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| - added unit tests for IPW estimators. | ||
| - added unit tests for IPW estimators. | ||
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| ### Github repository | ||
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| - added automated `R-cmd` check | ||
| - added automated `R-cmd` check | ||
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| ### Documentation | ||
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| - added documentation for `nonprob` function. | ||
| - added documentation for `nonprob` function. |