The diagonal density of Frank's copula | Computing the ``diagonal MLE'' | Session Information | Conclusion

Introduction | Archimedean copulas | Spearman's Rho for AMH

Introduction | Archimedean copulas | Nested Archimedean copulas | Outer power Archimedean copulas | Session Information | Conclusion

Introduction | Package `gsl' | Session Info

Introduction | Setting | Simulation | Simulation Results | Maximum Asymptotic Bias | Session Information

Introduction | Matrix Classes | More detailed examples of ``Matrix'' operations | Notes about S4 classes and methods implementation | Session Info

Introduction | Loader's Binomial Deviance D0(x,M) = bd0(x, M) | Accuracy of log1pmx(x) Computations | Accuracy of p1l1(t) Computations | Accuracy of stirlerr(x)=delta(x) Computations

1 Swiss Alps copulas of Hofert, Vrins (2013) | Lambda and its inverse | M and its inverse (for $M_i, i=1,2$): | S and its inverse (for $S_i, i=1,2$) | Wrappers for $p_1$ and $p_2$ and their inverses: | Define the copula $C$ | Draw n vectors of random variates from $C$ | 2 An example from Wolfgang Trutschnig and Manuela Schreyer | Define the Iterated Function System | Run chaos game B times | 3 Sierpinski tetrahedron | Session information

Generating the data | Estimation under unknown marginal parameters | Plot some true and estimated conditional quantile functions

Introduction | MCD and ``the Fast'' MCD (= fastmcd) Algorithm | Fast MCD Algorithm – General notation

Monotone psi-Functions | Redescenders

Introduction | History of the diptest package | 21st Century Improvement of Hartigan2's Table | The Dip in the Dip's Distribution | P-values for the Dip Test | Less Conservative Dip Testing | Session Info

Intro | The Easy Problem "in Theory" | Rounding to Integers | Rounding to non-zero Digits | Versions of round()ing - The Story | Alternative Approaches | Session information

Linear least squares calculations

The Matrix class structures | Matrix Transformations | Session Info

One factor: y f1 | One factor, one continuous: y f1 + x | Two (or more) factors, main effects only: y f1 + f2 | Interactions of two (or more) factors,.....

Gaussian Quantiles in R | Correcting qnorm(., log.p=TRUE) | Fully accurate asymptotic qnorm(., log.p=TRUE) | Concluding summary | Computational details, session information | relErrV() (from sfsmisc) | p.qnormAsy2() for optimal cutpoints

Introduction | Arithmetic with mpfr-numbers | ``All'' mathematical functions, arbitrarily precise | Arbitrarily precise matrices and arrays | Special mathematical functions | Integration highly precisely | Miscellaneous | Conclusion

Introduction: Not log() nor exp(), but log1p() and expm1() | log1p() and expm1() for log(1 - exp(x)) | Computation of log(1+exp(x)) | Conclusion

Examples (sampling and evaluating the log-likelihood) | Example 1: ((1,2), (3,4,5))-Gumbel | Example 2: (1, (2,3), 4, (5,6,7))-Gumbel | Example 3: (1, (2,3))-Gumbel | Plots of the negative log-likelihood | Determine the values of the negative log-likelihood on a grid | Plotting | Computing the MLE (via optimization)

1 Auxiliary functions | 2 Checking the various (smoothed) empirical copulas | 3 Application to show non-uniqueness of Sklar's Theorem for Bernoulli margins

Archimedean-Simplex copulas | Liouville copulas | Archimedean-Liouville copulas

Setup and auxiliary functions | 1 ACs vs AXCs vs NACs vs (different) HAXCs | 1.1 AC (Clayton copula) | 1.2 AXC (Clayton frailties and Gumbel EVC) | 1.3 NAC (nested Clayton) | 1.4 HAXC (hierarchical Clayton frailties and Gumbel EVC) | 1.5 HAXC (hierarchical Clayton frailties and hierarchical Gumbel EVC, same hierarchical structure) | 1.6 HAXC (hierarchical Clayton frailties and hierarchical Gumbel EVC, different hierarchical structure) | 2 EVCs vs HEVCs vs (different) HAXCs | 2.1 EVC | 2.2 HEVC | 2.3 HAXC (Clayton frailty and HEVC) | 2.4 HAXC (hierarchical Clayton frailties and EVC) | 2.5 HAXC (hierarchical Clayton frailties and HEVC, same hierarchical structure) | 2.6 HAXC (hierarchical Clayton frailties and HEVC, different hierarchical structure)

1 Quasi-random numbers for copula models via conditional distribution method | Independence copula | Clayton copula | $t$ copula with three degrees of freedom | Marshall--Olkin copula | 3d $t$ copula with three degrees of freedom | 3d R-Vine copula | 2 Quasi-random numbers for copula models via stochastic representations | 2.1 Colorized scatter plot | Colorized scatter plot (quasi-random numbers and CDM) | Colorized scatter plots (quasi-random numbers and MO) | 2.2 A variance-reduction example

Introduction | Non-central 2 probabilities: History of R's pnchisq.c

Intro | Session information

1 Auxiliary functions | 2 Setup | Plot Kendall's tau as a function in $\theta$ for different $\nu$ | Parameter specification | 3 Sampling and estimation | Sampling | Estimation | 4 Plots | Profile likelihood plots | -log-likelihood plot | Wireframe | Levelplot

.. intro .. | Better approximations using the Euler–Maclaurin formula | Session Information | Conclusion

1 Simulate data | 2 Fitting procedure based on the simulated data | 3 Simulate from the fitted time series model

Intro | Auxiliary functions | Joe's family | Easy case ($\tau=0.2$) | Harder case ($d=150$, $\tau=0.3$) | Even harder case ($d=180$, $\tau=0.4$) | Gumbel's family | Harder case ($d=150$, $\tau=0.6$) | Frank's family (an already hard case) | Session information

1 Auxiliary functions | Margins | (Nested) Clayton Lévy copula | Plotting | 2 Sampling paths | 4d positive Clayton Lévy copula | 4d positive nested Clayton Lévy copula

Introduction | Classes for dense matrices | Classes for sparse matrices