Is there a pmml conversion for the bnlearn package in r, so that bayesian networks can be accessed as an API over the web?
I have seen pmml conversion for some models like decision trees and linear regression, see here, but I haven't seen any for bnlearn.
Also, is there any other service which can be used as an API for bayesian network models built in R?
I haven't tried this personally but the package r2pmml from GitHub (it is not available from CRAN) may be an option for this. You would then you "Augustus" in Python.
Related
I went through the manuals available on the IBM website. However, there is no explanation on how to annotate a model for R API users. Does anyone have a hint?
I am looking at a couple of complex models that seem to need a lot of computational power. I am currently using the R package "glmmTMB" to account for spatio-temporal autocorrelation and random effects. In theory, glmmTMB should be able to run much faster using parallelization: https://cran.r-project.org/web/packages/glmmTMB/vignettes/parallel.html
If your OS supports OpenMP parallelization and R was installed using
OpenMP, glmmTMB will automatically pick up the OpenMP flags from R’s
Makevars and compile the C++ model with OpenMP support. If the flag is
not available, then the model will be compiled with serial
optimization only.
Instead of running these models on my personal maschine, I decided to set up a virtual maschine in a HPC environment. How can I install R using OpenMP on Ubuntu 20.04? I couldn't find anything on this topic.
I use R, but lately are trying to implement Model Predictive Control as applied in Control Engineering on my models, but it seems that this area in R is non-existent as compared to Matlab where it is quite easy to do System Identification and create transfer functions which can be deployed inside the Model Predictive Control module. Anybody knows where to look, or which packages to use for MPC and transfer functions in R to build MPC models?
There are a growing number of Python packages for control engineering. One option is to use the reticulate R package to interface to Python functions. Here are some Control Engineering packages in Python:
SciPy.signal for signal processing and system modeling
SymPy for Laplace transforms and differential equation analytic solutions
Control Systems Library
Chemical Process Control from Jeff Kantor, Notre Dame
Process Dynamics and Control in Python at BYU (my course)
Advanced Control and Machine Learning in Python at BYU (also my course)
Here is an example of running MPC in Python on the Temperature Control Lab.
I am wondering if anyone has faced this issue before. I use the package lmerTest to run mixed-effects models in R because it has a handy way of providing p-values. This package by default loads the most current version of the lme4 package. However, the lme4 current version has some issues and it sometimes doesn't converge, so the lme4 developers have made available a new package (named lme4.0), which is a bugfix-only version of the old pre-1.0 lme4. This works great, and the models usually converge, so that is what I use to analyze my data.
I would like to have lmerTest but have it load lme4.0 instead of the current version of lme4.Does anyone know how to achieve this?
Thanks for your help!
This isn't really feasible without serious hacking: essentially, take an older version of lmerTest, download the source, hack it to look for lme4.0 rather than lme4, and install locally. Or download (from the CRAN archives) and install older versions of lme4 and lmerTest (and pbkrtest: maintaining an archaic setup will get progressively more difficult, and you will have to backport or forego bug fixes as they appear in newer versions).
Since many of the problems with new lme4 have been cleaned up with the switch in default optimizers from Nelder-Mead to BOBYQA, my advice would be to run a range of comparisons between lme4.0 and lme4, convince yourself that there are no problems (and send information about persistent problems to the lme4 maintainers, who would greatly appreciate it!), and move on to the new version.
I do a lot of computational intelligence research. I have used Matlab almost exclusively as my programming medium for a decade or so. I am now trying to move to OSS. I have settled on R as my new environment.
After a long search for neural net software, the only Matlab-comparable OSS packages are Stuttgart NN and FANN (this can be debated another time =). The former doesn't appear to be maintained so I'd like to go with the latter. So my question is:
Does anyone have experience using R and FANN?
FANN has C++ bindings and R seems to have a couple of packages for a C++ interface, but since I'm a R newbie I need an idea of where exactly to start. Any guidance or recommendations would be appreciated.
Cheers.
I do not know anything abuot FANN but I can assure you that R has an actively maintained interface to the Stuttgart Neural Net Simulator (SNNS) library via the
RSNNS package --- as RSNNS happens to employ the
Rcpp package for interfacing R and C++ which I am involved in.