Showing 12 of total 12 results (show query)
gavinsimpson
permute:Functions for Generating Restricted Permutations of Data
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Maintained by Gavin L. Simpson. Last updated 8 months ago.
permutationrestricted-permutations
23 stars 13.28 score 538 scripts 488 dependents
nimble-dev
nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.
Maintained by Christopher Paciorek. Last updated 18 days ago.
bayesian-inferencebayesian-methodshierarchical-modelsmcmcprobabilistic-programmingopenblascpp
169 stars 12.97 score 2.6k scripts 19 dependents
henrikbengtsson
R.rsp:Dynamic Generation of Scientific Reports
The RSP markup language makes any text-based document come alive. RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'. Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries. RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things. Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'. RSP is ideal for self-contained scientific reports and R package vignettes. It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Maintained by Henrik Bengtsson. Last updated 1 years ago.
documentmarkupreportreproducibilityscience
31 stars 8.06 score 36 scripts 9 dependents
bioc
KEGGgraph:KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor
KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc.
Maintained by Jitao David Zhang. Last updated 5 months ago.
pathwaysgraphandnetworkvisualizationkegg
7.76 score 114 scripts 23 dependents
r-forge
fPortfolio:Rmetrics - Portfolio Selection and Optimization
A collection of functions to optimize portfolios and to analyze them from different points of view.
Maintained by Stefan Theussl. Last updated 10 days ago.
1 stars 6.65 score 179 scripts 2 dependents
tobiaskley
quantspec:Quantile-Based Spectral Analysis of Time Series
Methods to determine, smooth and plot quantile periodograms for univariate and multivariate time series.
Maintained by Tobias Kley. Last updated 9 years ago.
10 stars 5.84 score 46 scripts 1 dependents
modal-inria
RMixtCompUtilities:Utility Functions for 'MixtComp' Outputs
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility functions to facilitate the analysis of 'MixtComp' output.
Maintained by Quentin Grimonprez. Last updated 11 months ago.
clusteringcppheterogeneous-datamissing-datamixed-datamixture-modelstatistics
13 stars 5.19 score 2 scripts 1 dependents
ijayc
shinyr:Data Insights Through Inbuilt R Shiny App
It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
Maintained by Jayachandra N. Last updated 2 months ago.
3.00 score
cran
kequate:The Kernel Method of Test Equating
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Maintained by Björn Andersson. Last updated 3 years ago.
2 stars 2.90 score
jamescbell
gestate:Generalised Survival Trial Assessment Tool Environment
Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).
Maintained by James Bell. Last updated 2 years ago.
2 stars 2.60 score 8 scriptscran
readMLData:Reading Machine Learning Benchmark Data Sets in Different Formats
Functions for reading data sets in different formats for testing machine learning tools are provided. This allows to run a loop over several data sets in their original form, for example if they are downloaded from UCI Machine Learning Repository. The data are not part of the package and have to be downloaded separately.
Maintained by Petr Savicky. Last updated 10 years ago.
1 stars 1.00 score