This package provides a single unified interface for computing a wide variety of classical and modern correlation and association measures. Continuous methods include classical correlations (Pearson, Spearman, Kendall), modern dependence measures (distance correlation, maximal information coefficient, Hilbert-Schmidt independence criterion, Chatterjee's xi, Hoeffding's D, mutual information), robust correlations (biweight midcorrelation, percentage bend, Winsorized), ordinal correlations (polychoric, tetrachoric), partial and semi-partial correlations, and nonparametric measures (ball correlation, Bergsma-Dassios tau*). Categorical association measures (Cramer's V, phi coefficient, Goodman-Kruskal gamma, Somers D, contingency coefficient, Tschuprow's T) are available via moderncor_cat().

Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN 9780367469894, published in 2021 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.

This package provides functions to collapse a tidy data frame into matrices in a data frame and expand a data frame of matrices into a tidy data frame.

Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.

Set of tools for descriptive analysis of metaproteomics data generated from high-throughput mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, expressed as spectral counts, and to manipulate them in groups of metaproteins. This information can be represented using multiple visualization functions to portray the global metaproteome landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, taxonomic levels and/or functional annotation. The provided tools allow to implement flexible analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.

This package provides methods for controlling the median of the false discovery proportion (mFDP). Depending on the method, simultaneous or non-simultaneous inference is provided. The methods take a vector of p-values or test statistics as input.

Constructs multi-resolution thin-plate spline basis functions on the sphere for use in spatial regression and large-scale spatial prediction problems. Implements the basis system described in Huang, Huang, and Ing (2025) "Multi-Resolution Spatial Methods on the Sphere: Efficient Prediction for Global Data", Environmetrics, <doi:10.1002/env.70092>. Heavy computations are written in C++ via Rcpp with optional OpenMP parallelism.

Age-specific mortality rates are estimated and projected using the Kannisto, Lee-Carter and related methods as described in Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.

Extends the mlr3 ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the tf package. The package provides PipeOps for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional PCA'.

This package contains a set of tools for constructing and coercing into and from the "mdate" class. This date class implements ISO 8601-2:2019(E) and allows regular dates to be annotated to express unspecified date components, approximate or uncertain date components, date ranges, and sets of dates. This is useful for describing and analysing temporal information, whether historical or recent, where date precision may vary.

Create an interactive table of descriptive statistics in HTML. This table is typically used for exploratory analysis in a clinical study (referred to as Table 1').

Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.

Lightweight maps of mammals of the world. These maps are a comprehensive collection of maps aligned with the Mammal Diversity Database taxonomy of the American Society of Mammalogists. They are generated at low resolution for easy access, consultation and manipulation in shapefile format. The package connects to a binary backup hosted in the Digital Ocean cloud service and allows individual or batch download of any mammal species in the mdd taxonomy by providing the scientific species name.

Two functions for simulating the solution of initial value problems of the form g'(x) = G(x, g) with g(x0) = g0. One is an acceptance-rejection method. The other is a method based on the Mean Value Theorem.

This package provides a unified toolkit for classical test theory (CTT) item analysis of multiple-choice test data, including item difficulty (p-value), item discrimination (point-biserial correlation and upper-lower 27-percent discrimination index), per-distractor analysis (frequency, proportion, and discrimination), and Haladyna's distractor efficiency. A wrapper function returns a tidy mcq_analysis object with print, plot (difficulty-discrimination scatter), and APA-style table methods for direct inclusion in journal manuscripts. Implemented in pure R with no compiled code and minimal dependencies.

Quantify exposure-outcome causal effects with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the combination of inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data.

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using emmeans'.

Summarize multiple biomarker responses of aquatic organisms to contaminants using Cliffâ s delta, as described in Pham & Sokolova (2023) <doi:10.1002/ieam.4676>.

The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the training set and applying the model in the test set (Solanes et al., Neuroimage 2023, 265:119800). This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly, as described in (Solanes et al., Psychiatry Research: Neuroimaging 2021, 314:111313). It currently supports the estimation of sensitivity, specificity, balanced accuracy (for binary or multinomial variables), the area under the curve, correlation, mean squarer error, and hazard ratio for binomial, multinomial, gaussian, and survival (time-to-event) outcomes.

Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.

Various utilities to manipulate multivariate polynomials. The package is almost completely superceded by the spray and mvp packages, which are much more efficient.

This package provides a color palette generator inspired by Mexican politics, with colors ranging from red on the left to gray in the middle and green on the right. Palette options range from only a few colors to several colors, but with discrete and continuous options to offer greatest flexibility to the user. This package allows for a range of applications, from mapping brief discrete scales (e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays including dozens of shades graded from red to green.

The target of margaret is help to extract data from Minciencias to analyze scientific production in Colombia.