Utilities for Natural Language Processing.

Performs Bayesian wavelet analysis using individual non-local priors as described in Sanyal & Ferreira (2017) <DOI:10.1007/s13571-016-0129-3> and non-local prior mixtures as described in Sanyal (2025) <DOI:10.48550/arXiv.2501.18134>.

Additional nonlinear regression functions using self-start (SS) algorithms. One of the functions is the Beta growth function proposed by Yin et al. (2003) <doi:10.1093/aob/mcg029>. There are several other functions with breakpoints (e.g. linear-plateau, plateau-linear, exponential-plateau, plateau-exponential, quadratic-plateau, plateau-quadratic and bilinear), a non-rectangular hyperbola and a bell-shaped curve. Twenty eight (28) new self-start (SS) functions in total. This package also supports the publication 'Nonlinear regression Models and applications in agricultural research' by Archontoulis and Miguez (2015) <doi:10.2134/agronj2012.0506>, a book chapter with similar material <doi:10.2134/appliedstatistics.2016.0003.c15> and a publication by Oddi et. al. (2019) in Ecology and Evolution <doi:10.1002/ece3.5543>. The function 'nlsLMList' uses 'nlsLM' for fitting, but it is otherwise almost identical to 'nlme::nlsList'.In addition, this release of the package provides functions for conducting simulations for 'nlme' and 'gnls' objects as well as bootstrapping. These functions are intended to work with the modeling framework of the 'nlme' package. It also provides four vignettes with extended examples.

Implements likelihood inference based on higher order approximations for nonlinear models with possibly non constant variance.

Estimate the non-linear odds ratio and plot it against a continuous exposure.

Setup, run and analyze 'NetLogo' (<https://ccl.northwestern.edu/netlogo/>) model simulations in 'R'.
    'nlrx' experiments use a similar structure as 'NetLogos' Behavior Space experiments. 
    However, 'nlrx' offers more flexibility and additional tools for running and analyzing complex simulation designs and sensitivity analyses.
    The user defines all information that is needed in an intuitive framework, using class objects.
    Experiments are submitted from 'R' to 'NetLogo' via 'XML' files that are dynamically written, based on specifications defined by the user.
    By nesting model calls in future environments, large simulation design with many runs can be executed in parallel.
    This also enables simulating 'NetLogo' experiments on remote high performance computing machines.
    In order to use this package, 'Java' and 'NetLogo' (>= 5.3.1) need to be available on the executing system.