Concentration-Response Data Analysis using Curvep
Calculate the knee point on the exponential-like curve
Run Curvep on datasets of concentration-response data with a combinati...
Create concentration-response datasets that can be applied in the `run...
The Curvep function to process one set of concentration-response data
Default parameters of Curvep
Estimate benchmark response (BMR) for each dataset
Fit concentration-response data using Curve Class2 approach
Fit one set of concentration-response data using types of models
Get the default configurations for the Hill fit
Merge results from multiple rcurvep objects
Plot BMR diagnostic curves
Rcurvep: Concentration-Response Data Analysis using Curvep
Run parametric fits using types of models on concentration-response da...
Run Curvep on datasets of concentration-response data
Summarize the results from the parametric fitting using types of model...
Clean and summarize the output of rcurvep object
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).