Application of Ensemble Empirical Mode Decomposition and its variant based Support Vector regression model for univariate time series forecasting. For method details see Das (2020).<http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

This package provides functions and example datasets for Fechnerian scaling of discrete object sets. User can compute Fechnerian distances among objects representing subjective dissimilarities, and other related information. See package?fechner for an overview.

Multi-environment genomic prediction for training and test environments using penalized factorial regression. Predictions are made using genotype-specific environmental sensitivities as in Millet et al. (2019) <doi:10.1038/s41588-019-0414-y>.

This package provides an R interface to the GeoNetwork API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a GeoNetwork web-application and expose it to OGC CSW.

This package performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection.

Computes the ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies. Produces corresponding interaction plot and analysis of variance tables and p-values from several other tests of non-additivity.

This package implements a Shiny Item Analysis module and functions for computing false positive rate and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) <doi:10.1111/bmsp.12343>.

Assists in generating categorical clustered outcome data, estimating the Intracluster Correlation Coefficient (ICC) for nominal or ordinal data with 2+ categories under the resampling and method of moments (MoM) methods, with confidence intervals.

Common coordinate-based workflows involving processed chromatin loop and genomic element data are considered and packaged into appropriate customizable functions. Includes methods for linking element sets via chromatin loops and creating consensus loop datasets.

Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

Library of functions for the statistical analysis and simulation of Locally Stationary Wavelet Packet (LSWP) processes. The methods implemented by this library are described in Cardinali and Nason (2017) <doi:10.1111/jtsa.12230>.

Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.

Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.

Wrapper for minepy implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of minepy can be found at <http://minepy.readthedocs.io/en/latest>.

This package provides a set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.

Perform a Bayesian estimation of the ordinal exploratory Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data described by Culpepper, S. A. and Balamuta, J. J. (In Press) <doi:10.1080/00273171.2021.1985949>.

Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.

Basic statistical analyses. The package has been developed to be used in statistics courses at Bocconi University (Milan, Italy). Currently, the package includes some exploratory and inferential analyses usually presented in introductory statistics courses.

This package provides methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models.

This package wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results.

This package implements list environments. List environments are environments that have list-like properties. For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting.

This package simulates the process of installing a package and then attaching it. This is a key part of the devtools package as it allows you to rapidly iterate while developing a package.

Currently there are many functions in S-PLUS that are missing in R. To facilitate the conversion of S-PLUS packages to R packages, this package provides some missing S-PLUS functionality in R.

Create and customize interactive maps using the Leaflet JavaScript library and the htmlwidgets package. These maps can be used directly from the R console, from RStudio, in Shiny applications and R Markdown documents.