Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arXiv:1812.06948> for details.

Feature Ordering by Conditional Independence (FOCI) is a variable selection algorithm based on the measure of conditional dependence. For more information, see the paper: Azadkia and Chatterjee (2019),"A simple measure of conditional dependence" <arXiv:1910.12327>.

Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.

Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, Weibull', Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.

Two main functionalities are provided. One of them is predicting values with k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm. These are carried out in parallel using multiple threads.

This package provides a unified interface to large language models across multiple providers. Supports text generation, structured output with optional JSON Schema validation, and embeddings. Includes tidyverse-friendly helpers, chat session, consistent error handling, and parallel batch tools.

This package creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model. See <https://mareframe.github.io/mfdb/> for more information.

An efficient implementation of SCCI using Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.

Streamline searching, downloading and formatting of nature media files (e.g. audios, photos) from online repositories. The package offers functions for obtaining media metadata from online repositories, downloading associated media files and updating data sets with new records.

Agglomerative hierarchical clustering with a bespoke distance measure based on medication similarities in the Anatomical Therapeutic Chemical Classification System, medication timing and medication amount or dosage. Tools for summarizing, illustrating and manipulating the cluster objects are also available.

This package contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity. Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.

The zlog package offers functions to transform laboratory measurements into standardised z or z(log)-values. Therefore the lower and upper reference limits are needed. If these are not known they could be estimated from a given sample.

Originally inspired by Unity IMGUI (immediate mode GUI API).

Designed as an auxiliary module for raylib to create simple GUI interfaces using raylib graphic style (simple colors, plain rectangular shapes, wide borders...) but it can be adapted to other engines/frameworks.

Rspamd is an advanced spam filtering system that allows evaluation of messages by a number of rules including regular expressions, statistical analysis and custom services such as URL black lists. Each message is analysed by Rspamd and given a spam score.

This package provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.

Finds the best block diagonal matrix approximation of a symmetric matrix. This can be exploited for divisive hierarchical clustering using singular vectors, named HC-SVD. The method is described in Bauer (202Xa) <doi:10.48550/arXiv.2308.06820>.

Package for the analysis of categorical functional data. The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>. It also provides functions to perform basic statistical analysis on categorical functional data.

Calculates pointwise confidence intervals for the cumulative distribution function of the event time for current status data, data where each individual is assessed at one time to see if they had the event or not by the assessment time.

This package provides a set of tools for empirical analysis of diversity (a number and frequency of different types in a population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.

This package contains functions for the DivE estimator <doi:10.1371/journal.pcbi.1003646>. The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.

Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review).

Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.

Fits generalized linear models where the parameters are subject to linear constraints. The model is specified by giving a symbolic description of the linear predictor, a description of the error distribution, and a matrix of constraints on the parameters.