The short version | What does not change | What changes, and why | 1. horvitz_thompson() takes one probability argument | 2. Clustered fixed effects get a different default standard error | 3. Functions that are gone | Porting an existing script | What is new | Blocked designs whose blocks are not all the same shape | Speed | How this was checked | The estimatr issue list | Current status | References

Written with AI, and checked accordingly | How the checking works | Notation | The data used throughout | lm_robust | Coefficients | Weights | Heteroskedasticity-robust variance | Leverage at and above one | Cluster-robust variance | Absorbed fixed effects | Rank | Weights and the hat matrix: where Stata differs | Confidence intervals and testing | lm_lin | iv_robust | Variance | Which leverage | Correspondence with Stata | lh_robust | difference_in_means | Estimates | Variance for unblocked and clustered designs | Variance for blocked designs | What is refused | horvitz_thompson | What the declaration buys | Every promise in one table | What this does not cover | References

The experiment | Extreme value bounds | Double sampling | Poststratification | Sensitivity analysis | Trimming bounds | Dropping monotonicity | Standard errors | Reading the output | All of the estimators at once | References

The example data | lm_robust() | Clustered standard errors | Choosing a different variance estimator | Fixed effects | Weights | lm_lin() | iv_robust() | difference_in_means() | horvitz_thompson() | lh_robust() | Working with the output | In a simulation | Where to go next | References

The experiment | Extreme value bounds | Double sampling | Poststratification | Sensitivity analysis | Trimming bounds | Dropping monotonicity | Standard errors | Reading the output | All of the estimators at once | References

A hypothetical experiment | Simple random assignment | Complete random assignment | Simple and complete random assignment compared | Block random assignment | Balanced random assignment | Clustered assignment | Blocked and clustered assignment | Calculating probabilities of assignment | Best practices | Random assignment procedure = Random assignment function | Check probabilities of assignment directly | Save your random assignment

Terms used in this article | balanced_ra guarantees | 1. Demonstration | Uneven blocks | Balancing a covariate | 2. Logic | Three C++ implementations | 2.1 Two-arm pivot | One draw: four units, target 2.3 | Leftover pairing across blocks | C++: cube_pivot_pass | 2.2 Multi-arm cube | One draw: four units, three arms | C++: the fair bet, the window, and per-block landing | 2.3 Cube-on-X | One draw: $x=(1,2,3,6)$, $p_i=1/2$ | C++: the main loop | C++: finding a direction | 3. Examples | 3.1 One winner from unequal chances | 3.2 Two-arm counts with blocks | 3.3 Three arms, no blocks | 3.4 Heterogeneous probabilities | 3.5 Clusters | Clusters with a covariate | 3.6 formula plus blocks | 3.7 Multi-arm totals with blocks are not tight | 3.8 An attainable covariate total that is still missed | 3.9 An example of an attainable covariate total that never appears at all | 4. Analysing an assignment | 4.1 Weight by the probability of the condition received | 4.2 Standard errors: fine without formula, conservative with it | 5. Declaring the design | 6. Caveats | References

Written with AI, and checked accordingly | What a guarantee looks like here | The designs being checked | What randomizr promises | What this does not cover

Installation | Contents | Position Jitter Ellipse | Position Blue Noise | Position Sunflower | Position Honeycomb | Position Circle Pack | Example

A two-arm trial | A blocked experiment | A cluster-randomized experiment | Covariate adjustment | An effect that varies with a covariate | Noncompliance | Attrition

Basic syntax | More complex designs | Multi-arm trials | Why conditioning on the two-arm comparison matters | Comparing nested models | Three Arm Trial | Interaction terms | Arbitrary test statistics | Difference-in-variances | Balance Test | Sharp hypotheses that vary across units | Factorial designs | Conclusion