Setup | Data | Blocking efficiency and linkage results

Setup | Blocking for deduplication

Setup | Data | Integration with the reclin2 package | Usage with fastLink package | Usage with RecordLinkage package

Introduction | Software for non-probability samples | Methods for non-probability samples | Basic setup | Prediction-based approach | Prediction estimators | Mass imputation estimators | Variance estimators for the prediction approach | Inverse probability weighting | Variance estimators for the inverse probability weighting approach | Doubly robust approach | Parameters estimated separately | Minimization of the bias for doubly robust methods | Variance estimators for the doubly robust approach | Variable selection algorithms | The main function and the package functionalities | The nonprob function | Controlling the type of estimators | Controlling variance estimation | Data analysis example | Description of the data | Estimation | Propensity score approach | The doubly robust approach | Comparison of estimates | Advanced usage | Bootstrap approach for variance estimation | Classes and S3 methods | Summary and future work | Acknowledgements | Appendix: Algorithms for the MI-NN and MI-PMM estimators | References

Introduction | Software for capture-recapture with a single source | Theoretical background | How to estimate population size with a single source? | Available models | Fitting method | Algorithm 1: The modified IRLS algorithm used in the singleRcapture package | Bootstrap variance estimators | Algorithm 2: Semi-parametric bootstrap | Algorithm 3: Parametric bootstrap | The main function | The estimatePopsize function | Controlling variance estimation with controlPopVar | Data analysis example | Dataset | Testing marginal frequencies | Diagnostics | The stratifyPopsize function | Classes and S3Methods | Concluding remarks | Acknowledgements | Detailed information | The estimatePopsizeFit function | Structure of a family function | Implementing a custom singleRcapture family function | References

Overview | Quick start | Data-frame example | Respondents-only data | Response-only predictors | Survey design example | Practical guidance | Solver control and notes | References and further reading

Setup | Data | Linking datasets | Using basic functionalities of blocking package | Assessing the quality | Compare results

Overview | Notation and main objects | The vectorized score S_2 and matrix algebra | Mean estimation and survey weights | Arguments passed to exptilt (summary) | Connection to the EM viewpoint | Stopping criterion (maximum-norm) | Practical tutorial: from raw data to matrix operations (conceptual steps) | Why the vectorization matters (practical remarks) | Closing notes and references

Overview | Input data | Required Structure | Engine Configuration | Results | Survey designs

Overview | Input Data | Required Structure: | Engine Configuration | Results | Survey designs

Overview | Notation and Main Objects | Fixed Objects (Pre-computed) | Iterative Objects (The EM Algorithm) | The Expectation-Maximization (EM) Algorithm | Step 1.1 (E-Step): Compute Expected Nonrespondent Counts | Step 1.2 (M-Step): Update Odds Matrix | Step 2: Convergence | Final Estimates and Survey Weights | Survey Weights | Final Adjusted Counts | Final Proportion $\hat{\theta}_j$

Setup | Examples | Basic example | Example 1 -- census case | Using jointCalib package | Using survey package

Theory | Distributional entropy balancing | Distributional propensity score method | Packages | ATT with entropy balancing and other methods | Single variable: age | More variables | More variables: all variables | ATT with CBPS and DPS [work in progress]

Introduction | Calibration estimator for a total | Calibration estimator for a quantile | Joint calibration of totals and quantiles | References