Showing 8 of total 8 results (show query)
r-lib
testthat:Unit Testing for R
Software testing is important, but, in part because it is frustrating and boring, many of us avoid it. 'testthat' is a testing framework for R that is easy to learn and use, and integrates with your existing 'workflow'.
Maintained by Hadley Wickham. Last updated 1 months ago.
900 stars 20.99 score 74k scripts 471 dependents
r-forge
coin:Conditional Inference Procedures in a Permutation Test Framework
Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.
Maintained by Torsten Hothorn. Last updated 9 months ago.
11.70 score 1.6k scripts 73 dependents
optad
adoptr:Adaptive Optimal Two-Stage Designs
Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.
Maintained by Maximilian Pilz. Last updated 6 months ago.
1 stars 7.09 score 39 scripts 1 dependents
rohelab
fastRG:Sample Generalized Random Dot Product Graphs in Linear Time
Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.
Maintained by Alex Hayes. Last updated 7 months ago.
adjacency-matrixgraph-samplinglatent-factors
5 stars 4.52 score 22 scripts
bentaylor1
lgcp:Log-Gaussian Cox Process
Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.
Maintained by Benjamin M. Taylor. Last updated 1 years ago.
3.43 score 27 scripts
jeremyroos
gmgm:Gaussian Mixture Graphical Model Learning and Inference
Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.
Maintained by Jérémy Roos. Last updated 3 years ago.
bayesian-networksgaussian-mixture-modelsinferencemachine-learningprobabilistic-graphical-models
5 stars 3.40 score 7 scripts
christianroever
bspec:Bayesian Spectral Inference
Bayesian inference on the (discrete) power spectrum of time series.
Maintained by Christian Roever. Last updated 3 years ago.
2.71 score 86 scripts 2 dependents
pettermostad
lestat:A Package for Learning Statistics
Some simple objects and functions to do statistics using linear models and a Bayesian framework.
Maintained by Petter Mostad. Last updated 7 years ago.
2.28 score 64 scripts 1 dependents