Revert "math/*: Improve and expand pkg-descr"

The R-cran-combinat package provides a collection of essential routines

for combinatorial mathematics within the R environment. Combinatorics is

a branch of mathematics concerning the study of finite or countable

discrete structures.

This package offers functions to generate and manipulate various combinatorial

objects, including permutations, combinations, and partitions. It is

invaluable for researchers, statisticians, and data scientists who need

to perform tasks such as:

-   Generating all possible orderings of a set of items.

-   Selecting subsets of items without regard to their order.

-   Enumerating ways to divide a set into non-empty subsets.

By providing these fundamental combinatorial tools, R-cran-combinat

facilitates a wide range of applications in probability, statistics,

computer science, and experimental design.

Routines for combinatorics.

The R-cran-conf.design package provides a specialized set of tools

within the R environment for the construction and manipulation of

confounded and fractional factorial designs. These experimental designs

are fundamental in statistics and engineering for efficiently studying

the effects of multiple factors on an outcome, especially when resources

are limited.

Confounded designs allow for the study of a large number of factors

with a smaller number of experimental runs by strategically sacrificing

information about higher-order interactions. Fractional factorial designs

are a type of confounded design that uses a fraction of the full factorial

experiment, making them highly efficient for screening important factors.

This library simplifies the process of setting up and analyzing such

designs, making it invaluable for:

-   Experiment design in industrial and scientific research.

-   Quality improvement and process optimization.

-   Situations where a full factorial experiment is impractical due to

    cost or time constraints.

By offering these simple yet powerful tools, R-cran-conf.design enables

researchers and practitioners to conduct more efficient and insightful

experiments.

This small library contains a series of simple tools for constructing and

manipulating confounded and fractional factorial designs.

The R-cran-cvar package provides essential tools for risk management,

enabling the computation of Expected Shortfall (ES) and Value at Risk (VaR).

ES, also known as Conditional Value at Risk (CVaR), and VaR are key metrics

used to quantify potential financial losses in portfolios or investments.

This package offers high flexibility, allowing users to compute these

risk measures from various input types, including:

-   Quantile functions

-   Distribution functions

-   Random number generators

-   Probability density functions

It supports virtually any continuous distribution, making it adaptable

to diverse financial models. The functions are vectorized for efficient

computation across multiple arguments. The calculations are performed

directly from their definitions, as detailed by Acerbi and Tasche (2002).

Additionally, the package includes some support for GARCH (Generalized

Autoregressive Conditional Heteroskedasticity) models, further enhancing

its utility for analyzing financial time series volatility.

R-cran-cvar is an invaluable resource for financial analysts, risk managers,

and quantitative researchers working with R to assess and manage financial risk.

Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile

function, distribution function, random number generator or probability density

function. ES is also known as Conditional Value at Risk (CVaR). Virtually any

continuous distribution can be specified. The functions are vectorized over the

arguments. The computations are done directly from the definitions, see e.g.

Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH

models is provided, as well.

The R-cran-fracdiff package provides robust functionality for the

maximum likelihood estimation of parameters in fractionally differenced

ARIMA(p,d,q) models. These models are a powerful extension of traditional

ARIMA models, designed to capture long-range dependence in time series data,

where the 'd' parameter (differencing order) can be a non-integer value.

Fractionally differenced ARIMA models are particularly useful for

analyzing phenomena that exhibit persistent memory effects, such as:

-   Financial time series (e.g., stock prices, volatility)

-   Hydrological data (e.g., river flows, rainfall)

-   Environmental data (e.g., temperature anomalies)

-   Long-memory processes in various scientific and engineering fields

Based on the methodology by Haslett and Raftery (Applied Statistics, 1989),

this package offers a reliable and statistically sound approach to

modeling time series with fractional integration. It enables researchers

and practitioners in R to accurately estimate the parameters of these

complex models, leading to more precise forecasts and a deeper understanding

of long-memory processes.

Maximum likelihood estimation of the parameters of a fractionally

differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics,

1989).

The R-cran-gbutils package offers general-purpose utilities for numerical

and statistical computations in R, enhancing flexibility and ease of use.

Key functionalities include:

-   **Distribution Analysis**: Plotting density/distribution functions,

    numerically inverting distributions for quantiles, and simulating

    real/complex numbers from magnitude/argument distributions.

-   **Polynomial Manipulation**: Creating polynomials from roots

    (Cartesian or polar form).

-   **Programming Utilities**: Checking for NA identity, counting

    positional arguments, computing set intersections for multiple sets,

    identifying unnamed arguments, and graphing S4 classes.

This invaluable toolkit streamlines common tasks in data analysis,

statistical modeling, and numerical programming, boosting productivity

and analytical capabilities for R users.

Plot density and distribution functions with automatic selection of suitable

regions. Numerically invert (compute quantiles) distribution functions.

Simulate real and complex numbers from distributions of their magnitude and

arguments. Optionally, the magnitudes and/or arguments may be fixed in almost

arbitrary ways. Create polynomials from roots given in Cartesian or polar form.

Small programming utilities: check if an object is identical to NA, count

positional arguments in a call, set intersection of more than two sets, check

if an argument is unnamed, compute the graph of S4 classes in packages.