interflex 1.4.0

  1. GATE support across estimators: gate = TRUE now works with linear, lasso, dml, and grf estimators. When the moderator X is discrete, setting gate = TRUE estimates Group Average Treatment Effects (GATE) within each level of X. Use plot(out, by.group = TRUE) to visualize GATE with separate point estimates and confidence intervals per group.

  2. Unified g.est output: All estimators with gate = TRUE produce a unified $g.est field. The DML-specific $g.est.dml is retained as a deprecated alias for backward compatibility.

  3. Auto-trim xlim: When xlim is not specified and X is continuous, the plot automatically clips tails where the data is too sparse to support reliable CME estimation. Tails are only clipped if they have fewer than 10 observations or lack treatment variation. Users can always override with an explicit xlim.

  4. Default y-axis labels: Changed from “Marginal Effect of D on Y” to “CME of D on Y” (smooth curves) or “GATE of D on Y” (group-level plots with by.group = TRUE).

  5. New datasets: Added app_adiguzel2023, app_bb2024, and app_et2023 to the package data.

  6. Parallel computing: Switched from %dopar% to %dorng% (via the doRNG package) for reproducible parallel random number generation. The “Parallel computing” message now only prints when parallel is actually used.

  7. Quarto book: Restructured user manual with numbered chapters, separated computation from figure chunks, and added sections on GATE estimation.

  8. ggplot2 compatibility: Fixed deprecated size → linewidth, guides(colour = FALSE) → guides(colour = "none"), and removed aes_string() usage.

interflex 1.3.5

  1. Introduce the Lasso estimators.

  2. Bug fixes and improvements.

interflex 1.3.1-1.3.2

  1. Introduce the DML estimators

  2. Fix bugs

interflex 1.3.0

  1. Add support for DML estimators.

  2. Replace fastplm with fixest for estimating fixed effects models

  3. Add analytical standard errors for the kernel estimator.

interflex 1.2.1

  1. Allows users to conduct linear, binning and kernel estimation using glm functions including logit, probit, poisson and negative binomial model.

  2. Incorporate more flexible methods for uncertainty estimation, including simulation, delta method, and bootstrap.

  3. Because the estimated marginal effects/treatment effects in glm rely on the value of covariates Z, we now add an option Z.ref, by which users can pass in the values of covariates as what they want. The mean of each covariate in the data will be set as the default value of Z.ref.

  4. Incorporate the estimation of average treatment effects(if D is discrete) or average marginal effects(if D is continuous) of all observations.

  5. When the outcome takes binary values (0 and 1), the program supports four criteria in cross-validation for the kernel method, including Mean Squared Error(MSE), Mean Absolute Error(MAE), Cross Entropy and Area Under the Curve(AUC).

  6. For the binning estimator, the program allows likelihood ratio test(lr-test) and wald test, providing a double insurance for verifying the linear interaction effect (LIE) assumption.

  7. For all three estimators, the program allows a more flexible fully moderated model. When the option full.moderate is set to TRUE, all covariaes will interact with the moderater variable X in the same way as the treatment variable D does.

interflex 1.1.1

  1. Add a function inter.test to test the difference in treatment effects at two or three specific values in the moderator after the estimation.

interflex 1.1.0

  1. Accommodate discrete treatments variables (>2 treatment arms) interacting with continuous moderators; users can decide whether to draw the multiple marginal effects in one plot or in several by specifying the pool option.

  2. Incorporate adaptive bandwidth search in cross validation; optimize cross-validation when fixed effects are present.

  3. Add an umbrella function interflex (S3 method) to incorporate the functionalities of both inter.binning and inter.kernel while inter.binning and inter.kernel are deprecated (for usage, see old User’s Guide).

  4. Add a predict function, which allows users estimate and plot the expected value of Y given the values of the treatment, moderators, and controls.

  5. Allow users to test the difference in treatment effects at three different values in the moderate using the diff.values option

Note:

Starting from v.1.1.0, we incorporate the interation terms between the moderator X and all covariates Z in our models to reduce biases induced by their correlations (Blackwell and Olson 2019). Though not recommended, users can turn off this option by setting full.moderate = FALSE. In 22 published papers that we look into, we find the differences between the original model and the fully moderated model to be small.

Reference: Matthew Blackwell and Michael Olson (2019). “Reducing Model Misspecification and Bias in the Estimation of Interactions.” Mimeo, Harvard University.