Get text from images of text using Captricity Optical Character
    Recognition (OCR) API. Captricity allows you to get text from handwritten
    forms --- think surveys --- and other structured paper documents. And it can
    output data in form a delimited file keeping field information intact. For more
    information, read <https://shreddr.captricity.com/developer/overview/>.

Calibration of penalized criteria for model selection. The calibration methods available are based on the slope heuristics.

Functions to Accompany J. Fox and S. Weisberg, 
  An R Companion to Applied Regression, Third Edition, Sage, 2019.

Computer algebra via the 'SymPy' library (<https://www.sympy.org/>). 
  This makes it possible to solve equations symbolically, 
  find symbolic integrals, symbolic sums and other important quantities.

The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets
     that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).

Provides functions and command-line user interface to generate allocation sequence by covariate-adaptive randomization for clinical trials. The package currently supports six covariate-adaptive randomization procedures. Three hypothesis testing methods that are valid and robust under covariate-adaptive randomization are also available in the package to facilitate the inference for treatment effect under the included randomization procedures. Additionally, the package provides comprehensive and efficient tools to allow one to evaluate and compare the performance of randomization procedures and tests based on various criteria. See Ma W, Ye X, Tu F, and Hu F (2023) <doi: 10.18637/jss.v107.i02> for details.

Implements a class of univariate and multivariate spatial generalised linear mixed models for areal unit data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation using a single or multiple Markov chains. The response variable can be binomial, Gaussian, multinomial, Poisson or zero-inflated Poisson (ZIP), and spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution. A number of different models are available for univariate spatial data, including models with no random effects as well as random effects modelled by different types of CAR prior, including the BYM model (Besag et al., 1991, <doi:10.1007/BF00116466>) and Leroux model (Leroux et al., 2000, <doi:10.1007/978-1-4612-1284-3_4>). Additionally,  a multivariate CAR (MCAR) model for multivariate spatial data is available, as is a two-level hierarchical model for modelling data relating to individuals within areas. Full details are given in the vignette accompanying this package. The initial creation of this package was supported by the Economic and Social Research Council (ESRC) grant RES-000-22-4256, and on-going development has been supported by the Engineering and Physical Science Research Council (EPSRC) grant EP/J017442/1, ESRC grant ES/K006460/1, Innovate UK / Natural Environment Research Council (NERC) grant NE/N007352/1 and the TB Alliance.