R-universe search: exports:sphere

Showing 6 of total 6 results (show query)

tylermorganwall

rayrender:Build and Raytrace 3D Scenes

Render scenes using pathtracing. Build 3D scenes out of spheres, cubes, planes, disks, triangles, cones, curves, line segments, cylinders, ellipsoids, and 3D models in the 'Wavefront' OBJ file format or the PLY Polygon File Format. Supports several material types, textures, multicore rendering, and tone-mapping. Based on the "Ray Tracing in One Weekend" book series. Peter Shirley (2018) <https://raytracing.github.io>.

Maintained by Tyler Morgan-Wall. Last updated 4 days ago.

libx11 cpp

631 stars 10.87 score 188 scripts 8 dependents

andrewzm

FRK:Fixed Rank Kriging

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Maintained by Andrew Zammit-Mangion. Last updated 6 months ago.

cpp

71 stars 8.70 score 188 scripts 1 dependents

jayanilakshika

cardinalR:Collection of Data Structures

A collection of simple simulation datasets designed for generating Nonlinear Dimension Reduction representations techniques such as t-distributed Stochastic Neighbor Embedding, and Uniform Manifold Approximation and Projection. These datasets serve as a valuable resource for understanding the reliability of Nonlinear Dimension Reduction representations in various contexts.

Maintained by Jayani P.G. Lakshika. Last updated 5 days ago.

3.60 score

gmcmacran

tidydann:Add the 'dann' Model and the 'sub_dann' Model to the Tidymodels Ecosystem

Provides model specifications, tuning parameters for models in 'dann' package. Models based on Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

Maintained by Greg McMahan. Last updated 9 months ago.

3.30 score 20 scripts

cran

assist:A Suite of R Functions Implementing Spline Smoothing Techniques

Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.

Maintained by Yuedong Wang. Last updated 2 years ago.

fortran openblas

1.00 score

cran

gek:Gradient-Enhanced Kriging

Gradient-Enhanced Kriging as an emulator for computer experiments based on Maximum-Likelihood estimation.

Maintained by Carmen van Meegen. Last updated 16 days ago.

1.00 score