Showing 6 of total 6 results (show query)
bioc
clipper:Gene Set Analysis Exploiting Pathway Topology
Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype.
Maintained by Paolo Martini. Last updated 5 months ago.
68.1 match 4.66 score 19 scripts
baddstats
polyclip:Polygon Clipping
R port of Angus Johnson's open source library 'Clipper'. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
Maintained by Adrian Baddeley. Last updated 8 months ago.
64-bitclippercomputational-geometryminkowski-sumpolygon-clipping-algorithmpolygon-intersectionpolygon-offsettingpolygon-unionpolygonscpp
13.0 match 19 stars 10.31 score 27 scripts 553 dependents
rtelmore
ISAR:Introduction to Sports Analytics using R (ISAR) Data
We provide data sets used in the forthcoming textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2024). The package currently contains sixteen datasets and should be published in early 2024.
Maintained by Ryan Elmore. Last updated 8 months ago.
3.5 match 7 stars 4.02 score 3 scripts
hypertidy
plover:What the Package Does (One Line, Title Case)
Point in polygon.
Maintained by Michael D. Sumner. Last updated 3 years ago.
5.6 match 2 stars 2.00 score 1 scripts
mneunhoe
RGAN:Generative Adversarial Nets (GAN) in R
An easy way to get started with Generative Adversarial Nets (GAN) in R. The GAN algorithm was initially described by Goodfellow et al. 2014 <https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf>. A GAN can be used to learn the joint distribution of complex data by comparison. A GAN consists of two neural networks a Generator and a Discriminator, where the two neural networks play an adversarial minimax game. Built-in GAN models make the training of GANs in R possible in one line and make it easy to experiment with different design choices (e.g. different network architectures, value functions, optimizers). The built-in GAN models work with tabular data (e.g. to produce synthetic data) and image data. Methods to post-process the output of GAN models to enhance the quality of samples are available.
Maintained by Marcel Neunhoeffer. Last updated 2 years ago.
2.0 match 19 stars 3.98 score 7 scripts
pbs-software
PBSmapping:Mapping Fisheries Data and Spatial Analysis Tools
This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in 'Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include 'C++' code developed by Angus Johnson for the 'Clipper' library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory '.libPaths()', specifically in './PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.
Maintained by Rowan Haigh. Last updated 6 months ago.
0.5 match 11 stars 10.29 score 652 scripts 9 dependents