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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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I am currently working on time series project, I have tried SARIMA and Feed Forward neural networks for forecasting.
I found RNN(Recurrent Neural Network) as an interesting approach but am not finding any resources to understand RNN with implementation in R.
Does anyone have some examples of RNN and forecasting in R?
Thanks for the help!
May you should search for ltsm.
In R, you have here some exemples :
https://tensorflow.rstudio.com/blog/time-series-forecasting-with-recurrent-neural-networks.html
And perhaps thiscould be useful, Keras for R :
https://keras.rstudio.com/index.html
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I've been looking for a way to conduct group-based trajectory modeling in R with no avail. Something along the lines of what PROC TRAJ (http://www.andrew.cmu.edu/user/bjones/index.htm) accomplishes in SAS. Does anyone know of a similar package in R?
My outcome of interest (the model input) is categorical so i need something that can handle that.
The only package I've been able to find for this in R is crimCV. Here it is on Cran, and here is a working paper by the authors of the package on how it's done. I have not yet investigated this myself (and it seems like it hasn't been updated for years), but this page describes using it to fit a set of trajectories.
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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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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.
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Does anyone have a paper that explains how the Ckmeans.1d.dp algorithm works?
Or: what is the most optimal way to do k-means clustering in one-dimension?
Univariate k-means clustering can be solved in O(kn) time (on already sorted input) based on theoretical results on Monge matrices, but the approach was not popular most likely due to numerical instability and also perhaps coding challenges.
A better option is an O(knlgn) method that is now implemented in Ckmeans.1d.dp version 3.4.6. This implementation is as fast as heuristic k-means but offers guaranteed optimality, orders of magnitude better than heuristic k-means especially for large k's.
The generic dynamic programming solution by Richard Bellman (1973) does not touch upon specifics of the k-means problem and the implied runtime is O(kn^3).