Software

library("fortunes")
fortune("statistical thinking")
## 
## It is all too easy for statistical thinking to be swamped by programming tasks.
##    -- Brian D. Ripley
##       DSC 2001, Wien (March 2001)
fortune("cosmic radiation")
## 
## You say yourself it wasn't reproducible. So it could have been anything that
## "crashed" your R, cosmic radiation, a bolt of lightning reversing a bit in your
## computer memory, ... :-)
##    -- Martin Maechler (replying to a bug report)
##       R-devel (July 2005)

A particular focus of my work is devising efficient, in terms of computational complexity and implementation, algorithms and tools for applying the methods I develop to prominent data-analytic scenarios. I also engage in designing and implementing principled data analysis pipelines for application domains (e.g. sports).

Below is a list of the software I have (co-)developed and (co-)maintain in these directions.

Description       Package Links
betareg R Beta regression CRAN RForge
brglm R Bias reduction for binomial-response generalized linear models CRAN GitHub
brglm2 R Explicit and implicit methods for bias reduction in generalized linear models CRAN GitHub
brRasch R Maximum likelihood and bias reduction for fixed-effects Rasch models GitHub
cranly R Package directives and collaboration networks in CRAN CRAN GitHub
detectseparation R Detect and check for separation and infinite maximum likelihood estimates CRAN GitHub
enrichwith R Methods to enrich various R objects with extra components CRAN GitHub
GEEBRA R General estimating equations with or without bias-reducing adjustments GitHub
MEstimation R Methods for M-estimation of statistical models GitHub
PlackettLuce R Plackett-Luce models CRAN GitHub
profileModel R Tools for profiling inference functions for various model classes CRAN GitHub
semnar R Methods for constructing and interacting with databases of presentations CRAN GitHub
trackeR R Infrastructure for running and cycling data from GPS-enabled tracking devices CRAN GitHub
trackeRapp R Interface for the analysis of running, cycling and swimming data CRAN GitHub
waldi R Location-adjusted Wald statistics GitHub

cranly directives network for my R packages

Below, I am using my R package cranly to quickly build the directives network for my R packages.

library("magrittr")
library("cranly")
clean_CRAN_db() %>%
    build_network() %>%
    plot(author = "Ioannis Kosmidis", legend = FALSE, width = "100%")