R package on Ioannis Kosmidis
http://www.ikosmidis.com/tags/r-package/
Recent content in R package on Ioannis KosmidisHugo -- gohugo.ioenWed, 10 Jun 2020 00:00:00 +0000brquasi: Improved quasi-likelihood estimation
http://www.ikosmidis.com/post/news-brquasi-erum2020/
Wed, 10 Jun 2020 00:00:00 +0000http://www.ikosmidis.com/post/news-brquasi-erum2020/On 19 June 2020, I will be giving a talk at the eRum2020 conference titled “brquasi: Improved quasi-likelihood estimation”. The talk is about the brquasi R package, which provides a glm method for improving bias in quasi likelihood estimation.
That package is a spin-off of theoretical work with Nicola Lunardon on methods for reduced-bias M-estimation in general that require neither re-sampling (like the bootstrap does) nor full knowledge of the underlying distribution of the data (like other higher-order asymptotic methods do).detectseparation R package is on CRAN
http://www.ikosmidis.com/post/news-detectseparation-march-2020/
Thu, 26 Mar 2020 00:00:00 +0000http://www.ikosmidis.com/post/news-detectseparation-march-2020/detectseparation provides pre-fit and post-fit methods for detecting separation and infinite maximum likelihood estimates in generalized linear models with categorical responses.
Pre-fit methods The pre-fit methods apply on binomial-response generalized liner models such as logit, probit and cloglog regression, and can be directly supplied as fitting methods to the glm() function. They solve the linear programming problems for the detection of separation developed in Konis (2007), using ROI or lpSolveAPI.Making sense of CRAN: Package and collaboration networks
http://www.ikosmidis.com/post/news-cranly-user2019/
Sun, 07 Jul 2019 00:00:00 +0000http://www.ikosmidis.com/post/news-cranly-user2019/On 12 July 2019, I will be giving a talk at the useR! 2019 conference titled “Making sense of CRAN: Package and collaboration networks”. The talk is about what my cranly R package can do and how it can help users and developers understand CRAN better and the position of their (intended) packages within it.
Update (12 July 2019) The slides are available heretrackeRapp: interface & workflow for running, cycling and swimming data
http://www.ikosmidis.com/post/news-trackerapp-is-out/
Fri, 12 Apr 2019 00:00:00 +0000http://www.ikosmidis.com/post/news-trackerapp-is-out/🏃♀️🚴♂️🏊♀️
The first version of our trackeRapp R package is out!
trackeRapp provides the first fully interactive data analysis workflow for your runs, rides and swims! It is completely open source and written in R. Its backend is based on the trackeR R package and the frontend has been developed using the shiny R package.
To launch the trackeRapp interface locally, launch R and then
install.packages("trackeRapp") trackeRapp::trackeRapp() Then, hit “Load” and “Upload sample data set” to see what {trackeRapp} can do!cranly: Package and collaboration networks in CRAN
http://www.ikosmidis.com/post/news-cranly-oxfordrug2018/
Sat, 03 Nov 2018 00:00:00 +0000http://www.ikosmidis.com/post/news-cranly-oxfordrug2018/On 5 November 2018, I will be giving a talk at the R user group Oxford about “Package and collaboration networks in CRAN” using my cranly R package.
Update (06 Nov 2018) The slides and the code from my talk are here and here.trackeR v1.1 is on CRAN
http://www.ikosmidis.com/post/tracker-v11/
Mon, 24 Sep 2018 00:00:00 +0000http://www.ikosmidis.com/post/tracker-v11/The trackeR R package v1.1 is on CRAN. Version 1.1 of the package introduces full support for multi-sport workouts, numerous new visualisations, vast under-the-hood enhancements, bug fixes and more.
See NEWS for complete lists of new functionality, enhancements and bug fixes.
The package vignette provides a quick tour on its functionality. More detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality can be found here, which is an updated version ofModelling outcomes of soccer matches appears in Machine Learning
http://www.ikosmidis.com/post/news-soccer-paper/
Wed, 01 Aug 2018 00:00:00 +0000http://www.ikosmidis.com/post/news-soccer-paper/Modelling outcomes of soccer matches (joint work with Tsokos A, Narayanan S, Baio G, Cucuringu M, Whitaker G and Király F J) appeared in Machine learning.My cranly R package is on CRAN
http://www.ikosmidis.com/post/news-cranly-march-2018/
Wed, 28 Mar 2018 00:00:00 +0000http://www.ikosmidis.com/post/news-cranly-march-2018/cranly provides comprehensive methods for cleaning up and organising the information in the CRAN package database and for building package directives networks and collaboration networks. See also the package vignettes and the cranly GitHub page for more details.
Take a look at my Software page for the cranly directives network for my R packages and for how to make your own!trackeR paper appears in JSS
http://www.ikosmidis.com/post/news-tracker-paper/
Tue, 12 Dec 2017 00:00:00 +0000http://www.ikosmidis.com/post/news-tracker-paper/trackeR: Infrastructure for running and cycling data from GPS-enabled tracking devices in R (joint work with Hannah Frick) appeared online in the Journal of Statistical Software.My brglm2 R package is on CRAN
http://www.ikosmidis.com/post/news-brglm2/
Thu, 25 May 2017 00:00:00 +0000http://www.ikosmidis.com/post/news-brglm2/brglm2 provides various methods for mean and median bias reduction in the estimation of generalized linear models, along with pre-fit and post-fit methods for the detection of separation and of infinite maximum likelihood estimates in binomial response generalized linear models. See the package vignettes and the brglm2 GitHub page for details.My enrichwith R package is on CRAN
http://www.ikosmidis.com/post/news-enrichwith/
Thu, 18 May 2017 00:00:00 +0000http://www.ikosmidis.com/post/news-enrichwith/enrichwith provides the “enrich” method (verb) to enrich list-like R objects with new, relevant components. The current version can enrich objects of class ‘family’, ‘link-glm’, ‘lm’ and ‘glm’. The package also provides the ‘enriched_glm’ function that results in objects that carry various useful methods for generalized linear models (simulate, observed and expected information matrix, and model densities, probabilities, and quantiles at arbitrary parameter values). See the package vignettes and the enrichwith GitHub page for more details.