Software
Statistical software developed at the Institute of Biomedical Informatics, Faculty of Medicine, University of Ljubljana:

randomCancers functions for estimating an upper limit to the probability of random cancer using registry data as described in paper Random Cancers as Supported by Registry Data by Janez Stare, Robin Henderson and Nina Ružić Gorenjec
 functions
 programmed by Nina Ružić Gorenjec & Robin Henderson

re R function for calculating the Re measure of explained variation for the Cox model, Aalen's model or any of the parametric models included in the survreg function
 function
 programmed by Maja Pohar Perme

pseucheck R function for model diagnostics using pseudoobservations: for models fitted with 'coxph', 'crr', 'lm' and 'geese' function
 function
 requires packages: pseudo, survival, cmprsk, geese
 programmed by Maja Pohar Perme

koq function for S/SPLUS (available at StatLib)
 calculates Kent & O'Quigley's measure of dependence for censored data when dependence between time and covariates is modelled by Cox's model
 programmed by Andrej Blejec & Janez Stare

bj function for S/SPLUS/R (incorporated into Design package by Frank E. Harrell)
 fits the BuckleyJames distributionfree least squares multiple regression model to a possibly rightcensored response variable
 programmed by Janez Stare, Harald Heinzl & Frank E. Harrell
 measures of explained variation in survival analysis (other than koq) for S/SPLUS (available upon request)

pseu R function for Cox and additive model diagnostics using pseudoobservations for producing plots described in Pohar Perme M, Andersen PK. Checking hazard regression models using pseudoobservations. Statistics in Medicine 2008;27:53095328.
 function
 help file
 requires the following libraries: survival, Design, geepack (all available from CRAN) and timereg (available from http://staff.pubhealth.ku.dk/~ts/timereg.html)
 programmed by Maja Pohar

relsurv package for relative survival analysis in R (version 2.0, included as contributed package into CRAN)
 implementation of existing regression methods plus our own method (Stare, Henderson & Pohar: An individual measure of relative survival. JRSS Series C, 2005, 54(1), 115126)
 implementation of goodness of fit methods based on residuals (Stare, Pohar & Henderson: Goodness of fit of relative survival. Statistics in Medicine, 2005, 24(24), 39113925)
 paper describing the package (Pohar & Stare: Relative survival analysis in R. Computer Methods and Programs in Biomedicine, 2006, 81(3), 272278)
 programmed by Maja Pohar

interactive demonstration of relative survival
 website (form with links to instructions and references)
 based on the individual measure of relative survival and the relsurv package (see above)
 idea by Janez Stare, implementation by Brane Leskošek (Perl, Linux) and Maja Pohar (R)

R functions for calculating the reduced citation distribution (hindex) function
 function
 programmed by Maja Pohar Perme

chplot package for R (current version 1.2, included as contributed package into CRAN)
 produces augmented convex hull plots, which are a nice and informative way of displaying large amounts of grouped bivariate data
 paper describing the package (Vidmar & Pohar: Augmented convex hull plots: Rationale, implementation in R and biomedical applications. Computer Methods and Programs in Biomedicine, 2005, 78(1), 6974)
 package source (tar.gz)
 binary package for Windows (zip)
 idea and design by Gaj Vidmar, programmed by Maja Pohar

visualisation of concordance

two newly proposed graphical displays for the previously unaddressed task of visualising concordance (for ranked data, as for, e.g., Kendall's W)
 concordance bubbleplot (Excel workbook with macro and sample data)  depicts raw data, i.e., the actual ranks assigned to objects
 pincushion plot (interactive and batchmode code with sample data for jsplot; zipped)  depicts withinobject rank differences
 paper describing the methods (Vidmar & Rode: Visualising concordance. Computational Statistics, 2007, 22(3), in press)
 concordance parallelcoordinatesplot (SigmaPlot 2000 notebook; zipped)  another possibility (depicts pairs of ranks assigned to objects), which proved to be less practically useful (though esthetically appealing)
 ideas, design and implementation by Gaj Vidmar & Nino Rode

two newly proposed graphical displays for the previously unaddressed task of visualising concordance (for ranked data, as for, e.g., Kendall's W)

BlandAltman function for R
 displays the classic BlandAltman difference plot with limits of agreement, prints the mean difference and both agreement limits (along with 95% CIs) and returns a list of relevant parameters (the mean difference, its SD and SE, upper and lower agreement limits, their SE, and the t value used in the calculation of CIs)
 function and sample data (zip)
 programmed by Jaro Lajovic

Excel workbooks for demonstrational and teaching purposes

Poisson regression
 estimation procedure demonstrated with example 4.5 from A.J.Dobson, An Introduction to Generalized Linear Models (1st ed.), Chapman & Hall, 1990
 based on Excel's matrix functions, stepbystep iteration with macro
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

parallel coordinates plot
 static version for producing presentation/publication graphics
 for two variables, typical use is in conjunction with pairedsamples ttest, but such plot can also accompany the BlandAltman method (see below)
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

modified BoxCox power transformation towards normal distribution
 the lambda parameter can be either input manually or estimated with the maximumlikelihood method using Excel's Solver addin
 the transformation effect is demonstrated with automated histogram
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

interactive population pyramid (Eurostat's projection for Slovenia 20042051)
 the words are in Slovenian, but they are very few and with the image, everything should be understandable for anyone speaking any European language
 data obtained from the SISTAT database of the Statistical Office of the Republich of SLovenia
 inspired by the demographical model movies by Erich Neuwirth (University of Vienna, Austria)
 requires Excel 97 or higher
 implementation by Gaj Vidmar

BlandAltman plot
 implements the classic, simple, indispensable yet still too often overlooked methodcomparison procedure by Bland & Altman
 requires Excel 97 or higher
 core code by Michael Schacht Hansen (Section for Health Informatics, University of Aarhus, Dennmark); workbook, error trapping and embellishment by Jaro Lajovic & Gaj Vidmar

windowgram and coplot
 presents two methods, endorsed by Larry Weldon (Simon Fraser University, Vancouver, Canada) as underused but simple and ideal for inclusion into introductory statistics courses
 windowgrams are the simplest case of and hence the introduction to kernel density estimation; coplots is short for conditional plots, so it is just another name for what is also called panel plots, Trellis display or lattice graphics
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

dot plots
 presents simple and multiway dotplots as invented by William S. Cleveland and promoted by SPlus and R
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

univariate dotdensity plot
 presents the simplest yet very nice solution, i.e, characterbased cellchart, of what is also known as stacked dotplot
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

stemandleaf plot
 requires Excel 97 or higher
 macro by Nick Maxwell (Data Matters Resource Center); debugging and workbook design by Gaj Vidmar

KaplanMaier survival curve estimation
 requires Excel 97 or higher
 unauthorised English translation and modification of the workbook with macro by Shigenobu Aoki (Faculty of Social and Information Studies, Gunma University, Japan); produced by Gaj Vidmar

automated histogram binning (with random sampling from normal distribution)
 requires Excel 97 or higher
 idea and implementation by Gaj Vidmar

Iris dataset
 the most (perhaps too) often used (and sometimes abused) dataset in the field of statistics (too often abused in the fields of machine learning, data mining and information visualisation)
 reference for data collection: Anderson, E. (1935). The irises of the Gaspe Peninsula. Bulletin of the American Iris Society, 59, 25.
 reference for data analysis: Fisher, R.A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7 (2), 179188.
 a truly comprehensive presentation: Les Iris de Fisher ou Comment se familiariser avec le logiciel R (in French)

Poisson regression
Miscellaneous other software we've developed:

PowerPoint macro for counting all characters in a presentation
 programmed by Gaj Vidmar
 developed for translators to make them charging translations of presentations easier
 in addition to the characters in slides, the macro can count characters in the slide notes, handouts and titles
 works with English and Slovenian version of Microsoft® Office
 the macro is intended for Slovenian users, so the user interface is in Slovenian and the installation/removal instructions (below) are in Slovenian only
 the code is password protected

namestitev in odstranitev:
 datoteko StetjeZnakov.ppt shranite na disk
 zaženite PowerPoint in datoteko odprite, nato pa jo shranite kot dodatek (.ppa)
 kam se bo dodatek shranil, je odvisno od konfiguracije računalnika (npr. na Windows® 2000 v mapo C:\Documents and Settings\uporabniško_ime\Application Data\Microsoft\Addins)
 zaprite PowerPoint
 ponovno zaženite Powerpoint ter poskrbite, da so makri omogočeni (Orodja  Možnosti  Varnost  Varnost makrov...  nizka ali srednja)
 namestite nov dodatek (Orodja  Dodatki...  Dodaj nov...)
 v meniju Orodja se bo pojavila opcija "Štetje znakov"
 če ga ne želite več uporabljati, dodatek preko menija, iz katerega ste ga namestili, odstranite iz pomnilnika ali povsem odstranite
 tudi, če izberete popolno odstranitev, bo datoteka StetjeStrani.ppa ostala na disku, zato jo morate, če to želite, izbrisati ročno iz zgoraj omenjene mape