Suggests: fitdistrplus, survival, testthat (>= 3.0.0)

Packaged: 2021-07-05 08:46:37 UTC; PouillotRegis

Packaged: Tue Apr 7 05:13:34 2009; rforge

Title: Monte Carlo Based Tests for the Behrens Fisher Problem as an
Description: Monte Carol based tests for the Behrens Fisher Problem enhance the statistical power
and performs better than Welch's t-approximation, see Ullah et al. (2019).

Title: Global optimization using Monte Carlo and Quasi Monte Carlo
Description: The package performs global optimization combining Monte Carlo and Quasi Monte Carlo simulation with a local search. \n       The local searches can be easily speeded-up by using a network of local workstations.

Title: IRT models for multiple choice items (mcIRT)
Description: This package provides functions to estimate two popular
IRT-models: The old-school Nominal Response Model (Bock 1972)
and the quite recently developed Nested Logit Model (Suh & Bolt
2010). These are two models to examine multiple-choice items
and other multicategorial response formats.

Description: This package provides functions to estimate two popular IRT-models: The Nominal Response Model (Bock 1972) and the quite recently developed Nested Logit Model (Suh & Bolt 2010). These are two models to examine multiple-choice items and other multicategorial response formats.

Title: A Toolbox for the Multi-Criteria Minimum Spanning Tree Problem
Description: Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Additionally, a modular toolbox for the generation of multi-objective benchmark graph problems is included.
Suggests: testthat (>= 0.9.1), knitr, rmarkdown, gridExtra

Title: Estimating Node Fitness from a Single Network Snapshot by Markov
Description: A Markov chain Monte Carlo method is provided to estimate fitness from a single network snapshot. Conventional methods require the complete information about the appearance order of nodes and edges in the network. This package performs inference on the timeline space, and so does not require such information.