An R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. 3-parameter Hill equation from 'tcpl' package (Filer D et al.,  <doi:10.1093/bioinformatics/btw680>) and 4-parameter Hill equation from Curve Class2 approach (Wang Y et al.,  <doi:10.2174/1875397301004010057>) are available. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).

Defines classes and methods to process text-based
  cytogenetics using the CytoGPS web site, then import the results
  into R for further analysis and graphing.

Administrative regions and other spatial objects of the Czech Republic.

Provides pointwise and uniform estimation and inference methods for boundary discontinuity (BD) designs,
    a causal inference design that generalizes univariate regression discontinuity (RD) designs to settings with bivariate scores.
    Implements local polynomial methods for location-based and distance-based analyses, including sharp and fuzzy designs,
    data-driven bandwidth selection, pointwise confidence intervals, and uniform confidence bands. Methodology is developed in
    Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2505.05670> for location-based methods and
    Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2510.26051> for distance-based methods.
    For an overview and empirical guidance, see Cattaneo, Titiunik, and Yu (2026) <doi:10.48550/arXiv.2511.06474>.
    The companion software article is Cattaneo, Titiunik, and Yu (2025) <doi:10.48550/arXiv.2505.07989>.