Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.

A collection of evaluation metrics, including loss, score and utility functions,
that measure regression and classification performance.

A collection of evaluation metrics, including loss, score and
utility functions, that measure regression, classification and ranking performance.

Maximum Likelihood Estimation of the Niche Preemption Model
person("Jan", "Graffelman", role = c("aut", "cre"), email = "jan.graffelman@upc.edu")
Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.

Description: Various utilities for the Multiplicative
Multinomial distribution
R (>= 2.10.0), methods, magic, partitions, emulator, Oarray
(>= 1.4-3)

R (>= 2.10.0), methods, magic, partitions, emulator, Oarray
(>= 1.4-5)

Various utilities for the Multiplicative Multinomial distribution.
magic (>= 1.5-6), abind, emulator (>= 1.2-13), methods

magic (>= 1.5-6), abind, emulator (>= 1.2-13), methods,
mathjaxr

Single-Sample Classifier of Medulloblastoma Subtypes for
Medulloblastoma Patient Samples, Mouse Models, and Cell Lines
Description: A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human MB patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA plots and heatmaps.
Deena M.A. Gendoo  <deena.gendoo@utoronto.ca>, Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
GSVA (>= 1.13.1), kknn (>= 1.2-5), parallel, lattice,
pheatmap, R (>= 2.10)
MM2Sdata, Cancer, Classification, Microarray, Software,
Visualization