Showing 5 of total 5 results (show query)
pik-piam
magclass:Data Class and Tools for Handling Spatial-Temporal Data
Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).
Maintained by Jan Philipp Dietrich. Last updated 23 days ago.
5 stars 11.16 score 412 scripts 56 dependents
crunch-io
crunch:Crunch.io Data Tools
The Crunch.io service <https://crunch.io/> provides a cloud-based data store and analytic engine, as well as an intuitive web interface. Using this package, analysts can interact with and manipulate Crunch datasets from within R. Importantly, this allows technical researchers to collaborate naturally with team members, managers, and clients who prefer a point-and-click interface.
Maintained by Greg Freedman Ellis. Last updated 8 days ago.
9 stars 10.47 score 200 scripts 2 dependents
lbbe-software
MareyMap:Estimation of Meiotic Recombination Rates Using Marey Maps
Local recombination rates are graphically estimated across a genome using Marey maps.
Maintained by Aurélie Siberchicot. Last updated 26 days ago.
1 stars 5.30 score 20 scripts
qile0317
FastUtils:Fast, Readable Utility Functions
A wide variety of tools for general data analysis, wrangling, spelling, statistics, visualizations, package development, and more. All functions have vectorized implementations whenever possible. Exported names are designed to be readable, with longer names possessing short aliases.
Maintained by Qile Yang. Last updated 4 months ago.
scientific-computingutilitiesutilitycpp
2 stars 4.95 score 2 scripts
cran
RcppHMM:Rcpp Hidden Markov Model
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
Maintained by Roberto A. Cardenas-Ovando. Last updated 7 years ago.
1 stars 1.00 score