Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

Fit continuous-time correlated random walk models with time indexed
    covariates to animal telemetry data. The model is fit using the Kalman-filter on
    a state space version of the continuous-time stochastic movement process.

The crayon package is now superseded. Please use the 'cli'
    package for new projects.  Colored terminal output on terminals that
    support 'ANSI' color and highlight codes. It also works in 'Emacs'
    'ESS'. 'ANSI' color support is automatically detected. Colors and
    highlighting can be combined and nested. New styles can also be
    created easily.  This package was inspired by the 'chalk' 'JavaScript'
    project.

Provides color palettes based on crayon colors since the early 1900s. 
    Colors are based on various crayon colors, sets, and promotional palettes, most of
    which can be found at <https://en.wikipedia.org/wiki/List_of_Crayola_crayon_colors>. All 
    palettes are discrete palettes and are not necessarily color-blind friendly. 
    Provides scales for 'ggplot2' for discrete coloring.

Hardware-based support for 'CRC32C' cyclic redundancy checksum function
  is made available for 'x86_64' systems with 'SSE2' support as well as for 'arm64', 
  and detected at build-time via 'cmake' with a software-based fallback.  This
  functionality is exported at the 'C'-language level for use by other packages.
  'CRC32C' is described in 'RFC 3270' at <https://datatracker.ietf.org/doc/html/rfc3720>
  and is based on 'Castagnoli et al' <doi:10.1109/26.231911>.

Different approaches to censored or truncated regression with 
  conditional heteroscedasticity are provided. First, continuous 
  distributions can be used for the (right and/or left censored or truncated)
  response with separate linear predictors for the mean and variance. 
  Second, cumulative link models for ordinal data
  (obtained by interval-censoring continuous data) can be employed for
  heteroscedastic extended logistic regression (HXLR). In the latter type of
  models, the intercepts depend on the thresholds that define the intervals.
  Infrastructure for working with censored or truncated normal, logistic,
  and Student-t distributions, i.e., d/p/q/r functions and distributions3
  objects.