This package provides functions relating to time series analysis and computational finance.

This package provides an implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.

This package provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

This package provides an implementation of multiscale bootstrap resampling for assessing the uncertainty in hierarchical cluster analysis. It provides an AU (approximately unbiased) P-value as well as a BP (bootstrap probability) value for each cluster in a dendrogram.

Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem.

This package helps with quality checks, visualizations and analysis of mass spectrometry data, coming from proteomics experiments. The package is developed, tested and used at the Functional Genomics Center Zurich, where it is used mainly for prototyping, teaching, and having fun with proteomics data. But it can also be used to do data analysis for small scale data sets.

This package provides a wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

The Munsell package contains functions for exploring and using the Munsell colour system.

This package provides for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user-level customization and extension, while simplifying cross-class interoperability.

Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.

This package provides tools for multiple imputation of missing data in multilevel modeling. It includes a user-friendly interface to the packages pan and jomo, and several functions for visualization, data management and the analysis of multiply imputed data sets.

Constructs confidence intervals on the probability of success in a binomial experiment via several parameterizations

This package provides statistical procedures for calculating population-mean cosinor, non-stationary cosinor, estimation of best-fitting period, tests of population rhythm differences and more.

This package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. In addition, the package provides model selection for lasso, adaptive lasso and Ridge regression based on cross-validation.

Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015)

This package provides an extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for the special data format used for quantitative language comparison (QLC).

This package provides a collection of functions to help in the analysis of right-censored survival data. These extend the methods available in the survival package.

This package provides building blocks for allowing HTML widgets to communicate with each other, with Shiny or without (i.e., static .html files). It currently supports linked brushing and filtering.

Content-preserving transformations transformations of PDF files such as split, combine, and compress. This package interfaces directly to the qpdf C++ API and does not require any command line utilities. Note that qpdf does not read actual content from PDF files: to extract text and data you need the pdftools package.

This package provides utilities for processing and analyzing the files that are exported from a recorded Zoom meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.

Provides implementations of functions which have been introduced in R since version 3.0.0. The backports are conditionally exported which results in R resolving the function names to the version shipped with R (if available) and uses the implemented backports as fallback. This way package developers can make use of the new functions without worrying about the minimum required R version.

This package implements general purpose tools, such as functions for sampling and basic manipulation of Brazilian lawsuits identification number. It also implements functions for text cleaning, such as accentuation removal.

A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is an oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor.

This package enhances the R Optimization Infrastructure (ROI) package with the Embedded Conic Solver (ECOS) for solving conic optimization problems.