Not much yet, just a few (hopefully) useful links

• *Dimensional analysis (some examples)
• Principal components: prcomp() (princomp(); pc1() {Hmisc}, fast.prcomp() {gmodels}
• {pcaPP} (robust principal components by means of projection pursuit) {amap} (robust and parallelised methods for generalised and robust principal component analysis)
• {psy} psychometry methods
• cancor() {stats} Canonical Correlation.
• Independent Components: {fastICA} ( ICA) and Projection Pursuit)
• Multidimensional scaling: cmdscale() {stats} Classical MDS. sammon(), isoMDS() {MASS} Sammon and Kruskal's non-metric multidimensional scaling. indscal() {SensoMineR}
• Correspondence analysis: Packages FactoMineR and ade4, corresp(), mca() {MASS}

• Cluster task view (unsupervised classification)
• Machine Learning task view (supervised classification)

• CART Modeling: Package rpart
• Package party
Conditional trees
• Tree Based Methods (Quick R)

• CRAN Task View: Machine Learning & Statistical Learning
CRAN Task View: Multivariate Statistics