similar to: How to install libisl.so.19 on chromebook?

Thank you, it looks like I have already libisl: ``` apt search libisl Sorting... Done Full Text Search... Done libisl-dbg/oldstable 0.18-1 amd64 manipulating sets and relations of integer points bounded by linear constraints libisl-dev/oldstable 0.18-1 amd64 manipulating sets and relations of integer points bounded by linear constraints libisl15/oldstable,now 0.18-1 amd64

I don't know about the configuration. I installed R using the standard protocol for Chromebook http://blog.sellorm.com/2018/12/20/installing-r-and-rstudio-on-a-chromebook/ the rest, it was done by the system itself... On Tue, Jul 14, 2020 at 1:30 PM Dirk Eddelbuettel <edd at debian.org> wrote: > > > There is something wrong with your system / setup I did not notice first: >

On 14 July 2020 at 12:26, Luigi Marongiu wrote: | I am trying to install minpack.lm on R 3.3.3 (Debian version) on a | Chromebook. But I get this error: | ``` | > install.packages("minpack.lm") | Installing package into ?/home/marongiuluigi/R/x86_64-pc-linux-gnu-library/3.3? | (as ?lib? is unspecified) | trying URL 'https://cran.rstudio.com/src/contrib/minpack.lm_1.2-1.tar.gz'

There is something wrong with your system / setup I did not notice first: On 14 July 2020 at 13:08, Luigi Marongiu wrote: | Thank you, it looks like I have already libisl: | ``` | apt search libisl | Sorting... Done | Full Text Search... Done | libisl-dbg/oldstable 0.18-1 amd64 | manipulating sets and relations of integer points bounded by linear | constraints | | libisl-dev/oldstable 0.18-1

Hi Jack, > It is interesting though that LLVMPolly.so shows the linkage... > > /sw/opt/llvm-3.2/lib/LLVMPolly.so: > /sw/lib/libisl.10.dylib (compatibility version 11.0.0, current version 11.0.0) > /sw/lib/gmp5/libgmp.10.dylib (compatibility version 11.0.0, current version 11.5.0) > /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 169.3.0) >

On Tue, Oct 23, 2012 at 09:41:46AM +0200, Duncan Sands wrote: > Hi, > >> I looks like the code is calling dlopen() on LLVMPolly.so and it or something it links against has an initializer. The initialer is run before dlopen() returns and the crash is in the initializer. The message: >> >> dyld: fast lazy bind offset out of range (53437, max=7640) in image

On Mon, Oct 22, 2012 at 11:40:32AM -0700, Nick Kledzik wrote: > > On Oct 22, 2012, at 11:34 AM, Jack Howarth wrote: > > > Nick, > > I have uploaded the full walk with 'set env DYLD_PRINT_INITIALIZERS'. It didn't seem very informative > > as the dyld error occurs right after... > > > > (gdb) > >

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For

? Mon, 6 Nov 2023 17:53:49 +0100 Troels Ring <tring at gvdnet.dk> ?????: > Hence I wonder if I could somehow have non linear regression to find > the 3 pK values. Below is HEPESFUNC which delivers charge in the > fluid for known pKs, HEPTOT and SID. Is it possible to have > root-finding in the formula with nls? Sure. Just reformulate the problem in terms of a function that

On Oct 23, 2012, at 12:50 PM, Jack Howarth wrote: > On Mon, Oct 22, 2012 at 11:40:32AM -0700, Nick Kledzik wrote: >> >> On Oct 22, 2012, at 11:34 AM, Jack Howarth wrote: >> >>> Nick, >>> I have uploaded the full walk with 'set env DYLD_PRINT_INITIALIZERS'. It didn't seem very informative >>> as the dyld error occurs right after...

Thanks a lot! This was amazing. I'm not sure I see how the conditiion pK1 < pK2 < pK3 is enforced? - it comes from the derivation via generalized Henderson-Hasselbalch but perhaps it is not really necessary. Anyway, the use of Vectorize did the trick! Best wishes Troels Den 06-11-2023 kl. 19:19 skrev Ivan Krylov: > ? Mon, 6 Nov 2023 17:53:49 +0100 > Troels Ring <tring at

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

Dear all, I am using the nlsLM function to fit a Lorentzian function to my experimental data. The LM algorithm should allow to specify limits, but the upper limit appears not to work as expected in my code. The parameter 'w', which is peak width at half maximuim always hits the upper limit if the limit is specified. I would expect the value to be in-between the upper and lower limit with

Dear R useRs, I have a problem with nls.lm function of minpackl.lm package. I need to fit the Van Genuchten Model to a set of data of Theta and hydraulic conductivity with nls.lm function of minpack.lm package. For the first fit, the parameter estimates keep changing even after 1000 iterations (Th) and I have a following error message for fit of hydraulic conductivity (k); Reason for

On Sun, Oct 21, 2012 at 02:31:50PM -0700, Tobias Grosser wrote: > On 10/21/2012 12:47 PM, Jack Howarth wrote: >> On Sun, Oct 21, 2012 at 11:58:43AM -0700, Tobias Grosser wrote: >> ... >>> Does attached patch work for you? >>> >>> Tobi >>> >> >> Tobi, >> Yes, with this patch current llvm/polly svn builds and runs 'make

Dear R-users; I'm working with a a dataset that was previously used to fit a nonlinear model of the form: Y ~ a * (1 + b * log(1 - c * X^d)) The parameters published elsewhere are: a = 1.758863, b = .217217, c = .99031, and d = .054589 However, there is no way I can replicate this result. I've tried several options (including SAS) w/o success. The data is: X <-

We would like to fit parameters using a simulation with stochastic processes as theoretical values. We generate a simple exemple with nls.lm to see the logic and the problem: First without stochasticity (it is a dummy example, the fited value is simple the mean of a set of 10 numbers): #Ten numbers x <- 1:10 #Generate 10 Gaussian random number with mean=3 sd=1 simy <- rnorm(length(x),

I ran the following script. I satisfied the constraint by making a*b a single parameter, which isn't always possible. I also ran nlxb() from nlsr package, and this gives singular values of the Jacobian. In the unconstrained case, the svs are pretty awful, and I wouldn't trust the results as a model, though the minimum is probably OK. The constrained result has a much larger sum of squares.

I'm thinking of buying a Samsung Chromebook, largely for use while travelling. But I'd like to use it at home linked to my CentOS-6.4 server, rather than to the cloud. I'm wondering if this is practicable? I use LaTeX quite a lot, and I don't know if I could (a) download LaTeX to the Chromebook, (b) run LaTeX on the cloud, or (c) run LaTeX on my server from the Chromebook. Has

The package minpack.lm allows nonlinear regression problems to be addressed with a modification of the Levenberg-Marquardt algorithm based on the implementation of 'lmder' and 'lmdif' in MINPACK. Version 1.0-8 of the package is now available on CRAN. Changes in version 1.0-8 include: o possibility to obtain standard error estimates on the parameters via new methods for