Mon premier blog

Dynamic Linear Models with R (Use R) Giovanni Petris, Sonia Petrone, Patrizia Campagnoli
Publisher: Springer

The R language provides a rich environment We will first review data manipulation and programming in R, then cover theory and applications in R for topics such as linear and smooth regressions, survival analysis, mixed effects model, tree based methods, multivariate analysis, boot strapping and permutation. CGIwithR: Facilities for processing web forms using R. Dynamic Bradley-Terry modelling of sports tournaments. On the index of dissimilarity for lack of fit in log-linear and log-multiplicative models. Modelling subjective use of an ordinal response scale in a many period crossover experiment. Journal of Statistical Software 8(10), 1–8. OREpredict – a new package that enables scoring This is a typical scenario for use in, e.g., enterprise dashboards or within an application supporting campaign management or next-best-offer generation. OREdm – a new package that provides R access to several in-database Oracle Data Mining algorithms (Attribute Importance, Decision Tree, Generalized Linear Models, K-Means, Naïve Bayes, Support Vector Machine). Applied Statistics 51, 245–255. M, Varin, C and Firth, D (2013). Computational Statistics and Data Firth, D. This course provides students an opportunity to use the public domain and free software, R to perform statistical computing.