Utilities for Scheduling Functions to Execute Later with Event Loops

Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop.

Template Model Builder: A General Random Effect Tool Inspired by 'ADMB'

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines 'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through 'BLAS' and parallel user templates.

Mining Association Rules and Frequent Itemsets

Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. Hahsler, Gruen and Hornik (2005) .

Toolbox for Pseudo and Quasi Random Number Generation and Random Generator Tests

Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm () and WELL () generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some generator tests - the gap test, the serial test, the poker test, see, e.g., Gentle (2003) . Take a look at the Distribution task view of types and tests of random number generators. The package can be provided without the 'rngWELL' dependency on demand. Package in Memoriam of Diethelm and Barbara Wuertz.

Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Multiple Precision Arithmetic

Multiple Precision Arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the C library GMP (GNU Multiple Precision Arithmetic).

Efficient Serialization of R Objects

Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.

Quadratic Programming Solver using the 'OSQP' Library

Provides bindings to the 'OSQP' solver. The 'OSQP' solver is a numerical optimization package for solving convex quadratic programs written in 'C' and based on the alternating direction method of multipliers. See for details.

Tools for Working with Posterior Distributions

Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) .

Nonlinear Root Finding, Equilibrium and Steady-State Analysis of Ordinary Differential Equations

Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). Includes routines that: (1) generate gradient and jacobian matrices (full and banded), (2) find roots of non-linear equations by the 'Newton-Raphson' method, (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running, (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations by numerical differencing (using the method-of-lines approach). Includes fortran code.