RDHonest 1.0.1

Minor improvements and fixes

• Use covariate-adjusted outcome to compute nearest-neighbor variance estimator
• Drop collinear covariates automatically instead of throwing an error

RDHonest 1.0.0

New Features

• The function RDHonest computes estimates and confidence intervals for the
  regression discontinuity (RD) parameter in sharp and fuzzy designs. It
  supports covariates, clustering, and weighting. Confidence intervals are
  honest (or bias-aware), with critical values computed using the CVb
  function. Worst-case bias of the estimator is computed under either the Taylor
  or Hölder smoothness class.
• RDHonestBME computes confidence intervals in sharp RD designs with discrete
  covariates under the assumption assumption that the conditional mean lies in
  the "bounded misspecification error" class of functions, as considered in
  Kolesár and Rothe (2018).
• Support for plotting the data is provided by the function RDScatter
• The function RDSmoothnessBound computes a lower bound on the smoothness
  constant M, used as a parameter by RDHonest to calculate the worst-case
  bias of the estimator
• The function RDTEfficiencyBound calculates efficiency of minimax one-sided
  CIs at constant functions, or efficiency of two-sided fixed-length CIs at
  constant functions under second-order Taylor smoothness class.