This package performs statistical estimation and inference-related computations by accessing and executing modified versions of Fortran subroutines originally published in the Association for Computing Machinery (ACM) journal Transactions on Mathematical Software (TOMS) by Bunch, Gay and Welsch (1993) <doi:10.1145/151271.151279>. The acronym BGW (from the authors last names) will be used when making reference to technical content (e.g., algorithm, methodology) that originally appeared in ACM TOMS. A key feature of BGW is that it exploits the special structure of statistical estimation problems within a trust-region-based optimization approach to produce an estimation algorithm that is much more effective than the usual practice of using optimization methods and codes originally developed for general optimization. The bgw package bundles R wrapper (and related) functions with modified Fortran source code so that it can be compiled and linked in the R environment for fast execution. This version implements a function ('bgw_mle.R') that performs maximum likelihood estimation (MLE) for a user-provided model object that computes probabilities (a.k.a. probability densities). The original motivation for producing this package was to provide fast, efficient, and reliable MLE for discrete choice models that can be called from the Apollo choice modelling R package ( see <https://www.apollochoicemodelling.com>). Starting with the release of Apollo 3.0, BGW is the default estimation package. However, estimation can also be performed using BGW in a stand-alone fashion without using Apollo (as shown in simple examples included in the package). Note also that BGW capabilities are not limited to MLE, and future extension to other estimators (e.g., nonlinear least squares, generalized method of moments, etc.) is possible. The Fortran code included in bgw was modified by one of the original BGW authors (Bunch) under his rights as confirmed by direct consultation with the ACM Intellectual Property and Rights Manager. See <https://authors.acm.org/author-resources/author-rights>. The main requirement is clear citation of the original publication (see above).

This package facilitates RNA secondary structure plotting.

Import SGF (Smart Game File) into R.

rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks.

Estimating repeatability (intra-class correlation) from Gaussian, binary, proportion and Poisson data.

This is a sudoku game package with a shiny application for playing .

Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

Fast and efficient computation of rolling and expanding statistics for time-series data.

This package provides string and binary representations of objects for several formats and MIME types.

Algorithms for estimating robustly the parameters of a Gaussian, Student, or Laplace Mixture Model.

Floating Percentile Model with additional functions for optimizing inputs and evaluating outputs and assumptions.

Predict fish year-class strength by calibration regression analysis of multiple recruitment index series.

Relate

Interface to the ZeroMQ lightweight messaging kernel (see <https://zeromq.org/> for more information).

This package provides functionality to read files containing observations which consist of arbitrary key/value pairs.

This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.

This package implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.

This package provides popular sampling distributions C++ routines based in armadillo through a header file approach.

This package provides a fairly extensive and comprehensive interface to the graph algorithms contained in the Boost library.

Collection of tools for the analysis of the resilience of dynamic networks. Created as a classroom project.