Approximate Bayesian Inference via Sparse Grid Quadrature
Evaluation (BISQuE) for Hierarchical Models
Implementation of the 'bisque' strategy for approximate Bayesian posterior inference.  See Hewitt and Hoeting (2019) <arXiv:1904.07270> for complete details.  'bisque' combines conditioning with sparse grid quadrature rules to approximate marginal posterior quantities of hierarchical Bayesian models.  The resulting approximations are computationally efficient for many hierarchical Bayesian models.  The 'bisque' package allows approximate posterior inference for custom models; users only need to specify the conditional densities required for the approximation.
Rcpp (>= 0.12.4), RcppArmadillo, RcppEigen (>= 0.3.3.3.1)
A system with a recent-enough C++11 compiler (such
as g++-4.8 or later).

Cumulative History Analysis for Bistable Perception Time Series
person(given = "Alexander",
family = "Pastukhov",
role = c("aut", "cre"),
email = "pastukhov.alexander@gmail.com",
comment = c(ORCID = "0000-0002-8738-8591"))
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.
methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>=
2.18.1), dplyr, tibble, magrittr, glue, boot, future, purrr,
tidyr
Alexander Pastukhov [aut, cre]
(<https://orcid.org/0000-0002-8738-8591>)

bitmapped vectors of booleans (no NAs),
coercion from and to logicals, integers and integer subscripts;
fast boolean operators and fast summary statistics.
With bit vectors you can store true binary booleans {FALSE,TRUE} at the
expense of 1 bit only, on a 32 bit architecture this means factor 32 less
RAM and factor 32 more speed on boolean operations. With this speed gain it
even pays-off to convert to bit in order to avoid a single boolean operation
on logicals or a single set operation on (longer) integer subscripts, the
pay-off is dramatic when such components are used more than once.
Reading from and writing to bit is approximately as fast as accessing
standard logicals - mostly due to R's time for memory allocation. The package
allows to work with pre-allocated memory for return values by calling .Call()
directly: when evaluating the speed of C-access with pre-allocated vector
memory, coping from bit to logical requires only 70% of the time for copying
from logical to logical; and copying from logical to bit comes at a performance
penalty of 150%.