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I am looking for an R package which could provide a framework to interact with
Airborne-Laster-Scanning (ALS)-data and polygon-meshes. I guess it is pretty
straightforward to use RCPP for interfacing:
Libraries
OpenMesh
libLas
Pointclouds
CGAL
meshlab
Requested Features
To name a few:
Mesh-Lidar-interaction
refine/coarse triangulated mesh according to ALS
interpolate elevation from ALS
Inject points/lines/holes into the mesh
Mesh-Simplification
Reading and writing in different mesh-formats (.stl,.2dm, ...)
P.S.: I would also be interested in implementations in julia or haskell.
Related
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Is there a R package for this? I´m looking for something similar to qrencoder but in 1D
For now, my walkaround solution is to use python-barcode through reticulate but I would like to use a R library.
library(reticulate)
barcode <- import("barcode")
name <- barcode$generate('EAN13', '7750243002455', output='barcode_svg')
My ultimate goal is to show barcodes of my products on a Shiny app
Have a look at the zintr package, which uses zint.
barcode_print(8675309, "barcode1.png")
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I know with using pack 'real', we can use embedded R in Prolog. But I met problems to use this pack. And I didn't find simple examples of using this pack.
Can somebody give a simple example of this pack?
e.g. Loading Prolog values on to R variables and then call R functions on these values (generate a plot by R function or whatever).
The syntax is the same as the SWISH version I think so just look here:
http://swish.swi-prolog.org/example/Rserve.swinb
and
http://swish.swi-prolog.org/example/Rdataframe.swinb
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I am looking for an existing package that provides pathfinding algorithms for 2d data. I have a regular grid with scores and would like to start out with the A* algorithm.
I am surprised that there doesn't seem to be an R package dealing with such a task (obviously, googling for "a* algorithm in R" gives very unspecific results).
Does anyone know of an existing package and if there is none, can point me towards an efficient way of implementing the algorithm in R?
Thanks!
There is e1071 and igraph. Not sure if they do A*, but they seem to have other shortest past algorithms.
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Is there a package for approximating a function using Chebychev Polynomials?
I was formerly using Matlab and there was a package called Compecon that can approximate a function using Chebychev Polynomials.
I am wondering if there is any packages similar for Julia.
Or is there any packages in C or fortran or Python that can do this?
Thanka a lot!
You might want to check out ApproxFun.jl, it may be relevant.
https://github.com/QuantEcon/CompEcon.jl and related https://github.com/QuantEcon/BasisMatrices.jl have the capacity I believe.
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Does anyone know of any good implementation of group-lasso regularized linear regression in R (or even Matlab)?
Have you looked at GNU Octave? It does its work on command line so you can use it with any language that can read/write to file and execute shell commands to kick it off from within the program.
GNU Octave is featured in the Stanford Machine Learning Course on the chapter of linear regression with multiple variables.
There is the grplasso package in CRAN which I believe is what you are looking for.