Functional Linear Mixed Models for Densely Sampled Data (denseFLMM)
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

Insert and Extract ‘Reminders’ from Function Comments (remindR)
Insert/extract text ‘reminders’ into/from function source code comments or as the ‘comment’ attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual ‘tooltips’ for users, among other things.

Sampling from Conditional C- and D-Vine Copulas (CDVineCopulaConditional)
Provides tools for sampling from a conditional copula density decomposed via Pair-Copula Constructions as C- or D- vine. Here, the vines which can be used for such sampling are those which sample as first the conditioning variables (when following the sampling algorithms shown in Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>). The used sampling algorithm is presented and discussed in Bevacqua et al. (2017) <DOI:10.5194/hess-2016-652>, and it is a modified version of that from Aas et al. (2009) <DOI:10.1016/j.insmatheco.2007.02.001>. A function is available to select the best vine (based on information criteria) among those which allow for such conditional sampling. The package includes a function to compare scatterplot matrices and pair-dependencies of two multivariate datasets.

Bayesian Modelling of Extremal Dependence in Time Series (tsxtreme)
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.