Is there a R package for a Nested Logit or Probit model?
I've checked the bayesm and mnp packages, and they don't appear to have the capacity.
You could try the mlogit or VGAM packages.
Web searches work well via r-seek.
I don't see that I have the capacity to edit Dirk's postings (not surprising given our relative merits in R programming), but the link is actually to rseek.org, not r-seek.org
Other search facilities include Baron's site and of course Google with "r-project" in the search string:
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I am new to Julia and i estimated some multilevel regressions using Mixed Models. Everything worked perfectly fine but i would like to estimate the marginal means or marginal effects. In R there are two packages that i am aware of for that regard: emmeans and ggeffects. Are there similar packages in Julia?
In Julia, there is now Effects.jl, which uses the same technique as the effects package in R (which is what ggeffects uses for its computation).
Since your question is tagged mixed-models, you might also consider JellyMe4 which adds support for lme4/MixedModels to RCall.
Don't believe there is a good package for this at the moment, though you could use RCall.jl and process your data there. Or, if you don't mind doing it manually - you could possibly calculate it from the predict() method from GLM.jl
I am currently fitting a penalized logistic regression model using the package logistf (due to quasi-complete separation).
I chose this package over brglm because I found much more recommendations for logistf. However, the brglm seems to integrate better with other functions such as predict() or margins::margins(). In the documentation of brglm it says:
"Implementations of the bias-reduction method for logistic regressions can also be found in thelogistf package. In addition to the obvious advantage ofbrglmin the range of link functions that can be used ("logit","probit","cloglog"and"cauchit"), brglm is also more efficient computationally."
Has anyone experience with those two packages and can tell me whether I am overlooking a weakness in brglm, or can I just use it instead of logistf?
I'd be grateful for any insights!
Is there an R-Package I could use for Bayesian parameter estimation as an alternative to JAGS? I found an old question regarding JAGS/BUGS alternatives in R, however, the last post is already 9 years old. So maybe there are new and flexible gibbs sampling packages available in R? I want to use it to get parameter estimates for novel hierarchical hidden markov models with random effects and covariates etc. I highly value the flexibility of JAGS and think that JAGS is simply great, however, I want to write R functions that facilitate model specification and am looking for a package that I can use for parameter estimation.
There are some alternatives:
stan, with rstan R package. Stan looks well optimized but cannot do certain type of models (like binomial/poisson mixture model), since he cannot sample a discrete variable (or something like that...).
nimble
if you want highly optimized sampling based on C++, you may want to check Rcpp based solutions from Dirk Eddelbuettel
I found a package 'bivpois' for R which evaluates a model for two related poisson processes (for example, the number of goals by the home and the away team in a soccer game). However, this package seems to no longer be useable in newer versions of R.
Is there a reasonable way to modify the glm() function to do a similar process, or run this older package on my new version of R? I have found very little literature on these sorts of processes and have found very little in terms of easy implementation in other statistical packages like STATA.
Any suggestions would be much appreciated.
While CRAN does not host a current binary of bivpois, you can build the package from the archived source code (see http://cran.r-project.org/doc/manuals/R-exts.html#Checking-and-building-packages ). Building bivpois 0.50-3.1 from source (available at http://cran.r-project.org/src/contrib/Archive/bivpois/) works for me on R 2.15.0 Windows x64. The zipped Windows binary I built is available here: http://commondatastorage.googleapis.com/jthetzel-public/bivpois_0.50-3.1.zip .
You can feel free to refer to odds modelling and testing inefficiency of sports-bookmakersas I had modified the relevant functions inside bivpois package.
I'm looking for a an R package which can be used to train a Dirichlet prior from counts data. I'm asking for a colleague who's using R, and don't use it myself, so I'm not too sure how to look for packages. It's a bit hard to search for, because "R" is such a nonspecific search string. There doesn't seem to be anything on CRAN, but are there any other places to look?
I've only come across both R and the Dirichlet distribution in passing, so I hope I'm not too much off the mark.
This mailing list message seems to answer your question:
Scrolling through the results of
RSiteSearch("dirichlet") suggests some useful tools
in the VGAM package. The gtools package and
MCMC packages also have ddirichlet() functions
that you could use to construct a (negative log) likelihood
function and optimize with optim/nlmin/etc.
The deal, DPpackage and mix packages also may or may not provide what you need.
Then again, these are all still CRAN packages, so I'm not sure if you already found these and found them unsuitable.
As for searching for R, the R project site itself already provides a few links on its search page.