Estimates cumulative history for time-series for continuously
    viewed bistable perceptual rivalry displays. Computes cumulative history
    via a homogeneous first order differential process. I.e., it assumes
    exponential growth/decay of the history as a function time and perceptually
    dominant state, Pastukhov & Braun (2011) <doi:10.1167/11.10.12>.
    Supports Gamma, log normal, and normal distribution families.
    Provides a method to compute history directly and example of using the
    computation on a custom Stan code.

Provided are classes for boolean and skewed boolean vectors,
    fast boolean methods, fast unique and non-unique integer sorting,
    fast set operations on sorted and unsorted sets of integers, and
    foundations for ff (range index, compression, chunked processing).

Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. 
 These are useful for handling database keys and exact counting in +-2^63.
 WARNING: do not use them as replacement for 32bit integers, integer64 are not
 supported for subscripting by R-core and they have different semantics when 
 combined with double, e.g. integer64 + double => integer64. 
 Class integer64 can be used in vectors, matrices, arrays and data.frames. 
 Methods are available for coercion from and to logicals, integers, doubles, 
 characters and factors as well as many elementwise and summary functions. 
 Many fast algorithmic operations such as 'match' and 'order' support inter-
 active data exploration and manipulation and optionally leverage caching.

Contains the JIVE (joint inter and intra-specific model of variance evolution) model and other Bayesian models aimed at understanding trait evolution. The goal of the package is to join phylogenetic comparative models (PCM) that tend to integrate various type of data (individual observations, environmental data, fossil data) into a hierarchical Bayesian framework. It contains various PCMs as well as functions to join those models into a hierarchical Bayesian framework in a flexible and user friendly way. It contains various Markov chain Monte-Carlo (MCMC) algorithms, methods for model comparison and many plotting function for pre- and post-processing data visualization. Finally, this package integrates functions allowing bridges between 'R' and the 'BEAST2' implementations of PCMs. Kostikova A, Silvestro D, Pearman PB, Salamin N (2016) <doi:10.1093/sysbio/syw010>. Gaboriau T, Mendes FK, Joly S, Silvestro D, Salamin N (in prep).