Showing 3 of total 3 results (show query)
cran
lddmm:Longitudinal Drift-Diffusion Mixed Models (LDDMM)
Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.
Maintained by Giorgio Paulon. Last updated 1 years ago.
2.70 score
ma-kopka
symptomcheckR:Analyzing and Visualizing Symptom Checker Performance
Easily analyze and visualize the performance of symptom checkers. This package can be used to gain comprehensive insights into the performance of single symptom checkers or the performance of multiple symptom checkers. It can be used to easily compare these symptom checkers across several metrics to gain an understanding of their strengths and weaknesses. The metrics are developed in Kopka et al. (2023) <doi:10.1177/20552076231194929>.
Maintained by Marvin Kopka. Last updated 12 months ago.
2.70 score 1 scriptscran
scR:Estimate Vapnik-Chervonenkis Dimension and Sample Complexity
We provide a suite of tools for estimating the sample complexity of a chosen model through theoretical bounds and simulation. The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" <doi:10.31219/osf.io/evrcj>.
Maintained by Perry Carter. Last updated 3 months ago.
2.18 score