Remove discontinued designmatch package.

Author: NickCH-K

CausalInference.md

@@ -101,7 +101,7 @@ contact the maintainers.
     estimation of endogenous switching regression models), and
     `r pkg("riskRegression", priority = "core")` (for survival
     outcomes with or without competing risks). For parametric models, g-computation is the same as estimating average marginal effects, which can be achieved using `r pkg("margins")`, `r pkg("marginaleffects")`, `r pkg("modelbased")`, and `r pkg("stdReg")`.
--   *Matching* methods are implemented in `r pkg("MatchIt", priority = "core")`, which provides wrappers for a number of popular methods including propensity score matching and subclassification, (coarsened) exact matching, full matching, and cardinality matching; more specialized matching methods are implemented in some of the packages below, some of which MatchIt depends on. `r pkg("MatchThem")` provides a wrapper for MatchIt with multiply-imputed data. `r pkg("Matching", priority = "core")` performs nearest neighbor and genetic matching and implements Abadie and Imbens-style matching imputation estimators. `r pkg("optmatch")` performs optimal matching using network flows; several other packages rely on the same infrastructure, including `r pkg("DiPs")` (near-fine matching with directional penalties), `r pkg("matchMulti")` (optimal matching for clustered data), `r pkg("rcbalance")` and `r pkg("rcbsubset")` (optimal matching for refined balance), and `r pkg("approxmatch")` (near-optimal matching for multi-category treatments). Other packages include `r pkg("cem")` (coarsened exact matching), `r pkg("designmatch")` (optimization-based matching using mixed integer programming), `r pkg("stratamatch")` (matching and stratification in large datasets), `r pkg("FLAME")` (almost-matching-exactly via learned weighted Hamming distance), `r pkg("PanelMatch")` (matching with time-series cross-sectional data), and `r pkg("CausalGPS")` (generalized propensity score matching for continuous treatments).
+-   *Matching* methods are implemented in `r pkg("MatchIt", priority = "core")`, which provides wrappers for a number of popular methods including propensity score matching and subclassification, (coarsened) exact matching, full matching, and cardinality matching; more specialized matching methods are implemented in some of the packages below, some of which MatchIt depends on. `r pkg("MatchThem")` provides a wrapper for MatchIt with multiply-imputed data. `r pkg("Matching", priority = "core")` performs nearest neighbor and genetic matching and implements Abadie and Imbens-style matching imputation estimators. `r pkg("optmatch")` performs optimal matching using network flows; several other packages rely on the same infrastructure, including `r pkg("DiPs")` (near-fine matching with directional penalties), `r pkg("matchMulti")` (optimal matching for clustered data), `r pkg("rcbalance")` and `r pkg("rcbsubset")` (optimal matching for refined balance), and `r pkg("approxmatch")` (near-optimal matching for multi-category treatments). Other packages include `r pkg("cem")` (coarsened exact matching), `r pkg("stratamatch")` (matching and stratification in large datasets), `r pkg("FLAME")` (almost-matching-exactly via learned weighted Hamming distance), `r pkg("PanelMatch")` (matching with time-series cross-sectional data), and `r pkg("CausalGPS")` (generalized propensity score matching for continuous treatments).
 -   *Inverse propensity weighting* (IPW, also known as inverse probability of treatment weighting, IPTW) methods are implemented in `r pkg("WeightIt", priority = "core")`, which provides implementations and wrappers for several popular weighting methods for binary, multi-category, continuous, and longitudinal treatments. `r pkg("MatchThem")` provides a wrapper for WeightIt with multiply-imputed data. `r pkg("PSweight", priority = "core")` offers propensity score weighting and uncertainty estimation using M-estimation. `r pkg("inferference")` offers weighting methods in the context of interference. Several packages offer specialized methods of estimating balancing weights for various treatment types, which may or may not involve a propensity score: `r pkg("CBPS")` (generalized method of moments-based propensity score estimation for binary, multi-category, continuous, and longitudinal treatments), `r pkg("twang")` and `r pkg("twangContinuous")` (propensity score weighting using gradient boosting machines for binary, multi-category, continuous, and longitudinal treatments), `r pkg("sbw")` and `r pkg("optweight")` (optimization-based weights using quadratic programming), and `r pkg("ebal")` (entropy balancing). `r pkg("mvGPS")` estimates weights for multivariate treatments using WeightIt's infrastructure. *Matching-adjusted indirect comparison*, a relative of propensity score weighting when unit-level data is only available for some groups, is available in `r pkg("maicChecks")` and `r pkg("optweight")` (using the `optweight.svy()` function).
 -   *Doubly robust methods* involve both a treatment and outcome model. Augmented IPW (AIPW) is implemented in `r pkg("AIPW")`, `r pkg("PSweight")`, `r pkg("DoubleML")`, `r pkg("grf")` (functions `causal_forest` followed by `average_causal_effect`), and `r pkg("causalweight")`. Targeted maximum likelihood estimation (TMLE, also known as targeted minimum loss-based estimation) is available in `r pkg("drtmle")`, `r pkg("tmle", priority = "core")`, `r pkg("ctmle")` (for TMLE with variable selection), `r pkg("ltmle")` (for longitudinal data), and `r pkg("AIPW")`.
 -   *Difference in differences* methods are implemented in