R Wrappers for EXPOKIT; Other Matrix Functions

Wraps some of the matrix exponentiation utilities from EXPOKIT (< http://www.maths.uq.edu.au/expokit/>), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well. NOTE: In case FORTRAN checks temporarily get rexpokit archived on CRAN, see archived binaries at GitHub in: nmatzke/Matzke_R_binaries (binaries install without compilation of source code).

Joint Mean and Covariance Estimation for Matrix-Variate Data

Jointly estimates two-group means and covariances for matrix-variate data and calculates test statistics. This package implements the algorithms defined in Hornstein, Fan, Shedden, and Zhou (2018) .

Analysis of R Code for Reproducible Research and Code Comprehension

Tools for analyzing R expressions or blocks of code and determining the dependencies between them. It focuses on R scripts, but can be used on the bodies of functions. There are many facilities including the ability to summarize or get a high-level view of code, determining dependencies between variables, code improvement suggestions.

Dimension Reduction, Regression and Discrimination for Chemometrics

Data exploration and prediction with focus on high dimensional data and chemometrics. The package was initially designed about partial least squares regression and discrimination models and variants, in particular locally weighted PLS models (LWPLS). Then, it has been expanded to many other methods for analyzing high dimensional data. The name 'rchemo' comes from the fact that the package is orientated to chemometrics, but most of the provided methods are fully generic to other domains. Functions such as transform(), predict(), coef() and summary() are available. Tuning the predictive models is facilitated by generic functions gridscore() (validation dataset) and gridcv() (cross-validation). Faster versions are also available for models based on latent variables (LVs) (gridscorelv() and gridcvlv()) and ridge regularization (gridscorelb() and gridcvlb()).

A Byte-Pair-Encoding (BPE) Tokenizer for OpenAI's Large Language Models

A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.

'R' Interface to the 'Diariodeobras' Application

Provides a set of functions for securely storing 'API' tokens and interacting with the < https://diariodeobras.net> system. Includes convenient wrappers around the 'httr2' package to perform authenticated requests, retrieve project details, tasks, reports, and more.

Secure and Intuitive Access to 'Plug' Interface

Provides a secure and user-friendly interface to interact with the 'Plug' < https://plugbytpf.com.br> 'API'. It enables developers to store and manage tokens securely using the 'keyring' package, retrieve data from 'API' endpoints with the 'httr2' package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with 'Plug' ecosystem.

Goodness-of-Fit Tests for Capture-Recapture Models

Performs goodness-of-fit tests for capture-recapture models as described by Gimenez et al. (2018) . Also contains several functions to process capture-recapture data.

Bayesian Model to Estimate Population Trends from Counts Series

Infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers < https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.

Fast Network Modularity and Roles Computation by Simulated Annealing (Rgraph C Library Wrapper for R)

Provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, ).