Modelling Framework for the Estimation of Salmonid Abundance

A set of functions to estimate capture probabilities and densities from multipass pass removal data.

Import Professional Baseball Data from 'Retrosheet'

A collection of tools to import and structure the (currently) single-season event, game-log, roster, and schedule data available from < https://www.retrosheet.org>. In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse. This package does the parsing on those files, returning the requested data in the most practical R structure to use for sabermetric or other analyses.

Interface to the 'sparseLM' Levenberg-Marquardt Library

Provides an R interface to the 'sparseLM' C library for large-scale nonlinear least squares problems with arbitrarily sparse Jacobians. The underlying solver implements a sparse variant of the Levenberg-Marquardt algorithm for minimizing sum-of-squares objective functions, supports user-supplied analytic Jacobians or finite-difference approximation, and is designed to exploit sparsity for improved memory use and performance. This package exposes the solver in R and uses sparse matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines through the 'Matrix' package interface. Methods from the C library are described in Lourakis (2010) .

DATRAS Trawl Survey Database Web Services

R interface to access the web services of the ICES (International Council for the Exploration of the Sea) DATRAS trawl survey database < https://datras.ices.dk/WebServices/Webservices.aspx>.

Read and Process 'Pamguard' Binary Data

Functions for easily reading and processing binary data files created by 'Pamguard' (< https://www.pamguard.org/>). All functions for directly reading the binary data files are based on 'MATLAB' code written by Michael Oswald.

Functions to Support the ICES Transparent Assessment Framework

Functions to support the ICES Transparent Assessment Framework < https://taf.ices.dk> to organize data, methods, and results used in ICES assessments. ICES is an organization facilitating international collaboration in marine science.

Gillespie's Stochastic Simulation Algorithm (SSA)

Provides a simple to use, intuitive, and extensible interface to several stochastic simulation algorithms for generating simulated trajectories of finite population continuous-time model. Currently it implements Gillespie's exact stochastic simulation algorithm (Direct method) and several approximate methods (Explicit tau-leap, Binomial tau-leap, and Optimized tau-leap). The package also contains a library of template models that can be run as demo models and can easily be customized and extended. Currently the following models are included, 'Decaying-Dimerization' reaction set, linear chain system, logistic growth model, 'Lotka' predator-prey model, Rosenzweig-MacArthur predator-prey model, 'Kermack-McKendrick' SIR model, and a 'metapopulation' SIRS model. Pineda-Krch et al. (2008) .

API Client and Dataset Management for the Demographic and Health Survey (DHS) Data

Provides a client for (1) querying the DHS API for survey indicators and metadata (< https://api.dhsprogram.com/#/index.html>), (2) identifying surveys and datasets for analysis, (3) downloading survey datasets from the DHS website, (4) loading datasets and associate metadata into R, and (5) extracting variables and combining datasets for pooled analysis.

Markdown Parser Implemented using the 'MD4C' Library

Provides an R wrapper for the 'MD4C' (Markdown for 'C') library. Functions exist for parsing markdown ('CommonMark' compliant) along with support for other common markdown extensions (e.g. 'GitHub' flavored markdown, 'LaTeX' equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

Optimization via Subsampling (OPTS)

Subsampling based variable selection for low dimensional generalized linear models. The methods repeatedly subsample the data minimizing an information criterion (AIC/BIC) over a sequence of nested models for each subsample. Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models.