This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data.

This package provides tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.

This package provides software for the book Spectral Analysis for Physical Applications, Donald B. Percival and Andrew T. Walden, Cambridge University Press, 1993.

The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications.

This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data.

This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC).

This package provides functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data.

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

This package provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>.

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations errors association.

Convert Ensembl gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including Entrez', HGNC', and UniProt'.

This package performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.

This package provides a simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.

Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.

Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).

Bindings to system utilities found in most Unix systems such as POSIX functions which are not part of the Standard C Library.

Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <https://dataexplorer.wittgensteincentre.org/>.

Helps to fit thermal performance curves (TPCs). rTPC contains 49 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

Automates the download and processing of historical weather data from the Brazilian National Institute of Meteorology (INMET). It resolves formatting inconsistencies in raw CSV files across different years, removes structural artifacts, standardizes column names, converts timestamps to local Brazilian time zones, and outputs tidy data frames ready for analysis. Data are retrieved from <https://portal.inmet.gov.br/dadoshistoricos>.

This package provides functions to deal with binary classification problems in the presence of imbalanced classes. Synthetic balanced samples are generated according to ROSE (Menardi and Torelli, 2013). Functions that implement more traditional remedies to the class imbalance are also provided, as well as different metrics to evaluate a learner accuracy. These are estimated by holdout, bootstrap or cross-validation methods.

R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.

RtMidi is a set of C++ classes (RtMidiIn, RtMidiOut, and API specific classes) that provide a common cross-platform API for realtime MIDI input/output.