Implements safe policy learning under regression discontinuity designs
    with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>.
    The learned cutoffs are guaranteed  to perform no worse than the existing
    cutoffs in terms of overall outcomes. The 'rdlearn' package also includes
    features for visualizing the learned cutoffs relative to the baseline and
    conducting sensitivity analyses.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.

Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. 
  Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas.
  It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.

Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages, as described in Rodiger et al. (2017) <doi:10.1093/bioinformatics/btx528>.
    Contains a dendrogram visualization for the structure of RDML object and GUI for RDML editing.