Imports: graphics, grDevices, mathjaxr, stats, scales, zoo
Description: Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model.

Description: Provides tools to directly model underlying population dynamics using date datasets (radiocarbon and other) with a Continuous Piecewise Linear (CPL) model framework. Various other model types included. Taphonomic loss included optionally as a power function. Model comparison framework using BIC. Package also calibrates 14C samples, generates Summed Probability Distributions (SPD), and performs SPD simulation analysis to generate a Goodness-of-fit test for the best selected model. Details about the method can be found in Timpson A., Barberena R., Thomas M. G., Mendez C., Manning K. (2020) <doi:10.1098/rstb.2019.0723>.

Title: Adoption Probability, Triers and Users Rate of a New Product
person("Mickey", "Kislev", email = "mickeykislev@researchgap.ac", role=c("cre")),
Description: Calculating users of a product based on the prevalence of triers in the population. The measuring of triers is relatively easy. It is just a question of whether a person tried a product even once in his life or not. While measuring the rate of people who also adopt it as part of their life is more complicated since the adoption of an innovative product is a subjective view of the individual. Mickey Kislev and Shira Kislev developed a formula to calculates the prevalence of users of a product to overcome this difficulty. The current calculator assists in calculating the users of a product based on the prevalence of triers in the population. See for: Kislev, M., and S. Kislev (2020) <doi:10.5539/ijms.v12n4p63>.