Provides functions to simulate and analyze data for a regression model with an interval censored covariate, as described in Morrison et al. (2021) <doi:10.1111/biom.13472>.

Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.

Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.

A plug in for using 'WinEdt' as an editor for R.

An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.

Implementation of the Integrated Simple Weighted Sum Product Method (WISP), a multiple criteria sorting method create by Dragisa Stanujkic (2021) <doi:10.1109/TEM.2021.3075783>.

Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.