This package aims to identify candidate genes that are differentially methylated between cases and controls. It applies Student's t-test and delta beta analysis to identify candidate genes containing multiple CpG sites.

This package provides geometries to plot network objects with the ggplot2 package.

This package performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

This package provides p-values in type I, II or III anova and summary tables for lmer model fits via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.

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.

This package provides a framework for text mining applications within R.

This package estimates the parameters in Dirichlet-Multinomial and computes log-likelihoods.

This package provides tools for functional linear modeling and analysis of actigraphy data.

colorout is an R package that colorizes R output when running in terminal emulator.

R STDOUT is parsed and numbers, negative numbers, dates in the standard format, strings, and R constants are identified and wrapped by special ANSI scape codes that are interpreted by terminal emulators as commands to colorize the output. R STDERR is also parsed to identify the expressions warning and error and their translations to many languages. If these expressions are found, the output is colorized accordingly; otherwise, it is colorized as STDERROR (blue, by default).

You can customize the colors according to your taste, guided by the color table made by the command show256Colors(). You can also set the colors to any arbitrary string. In this case, it is up to you to set valid values.

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

The r-nleqslv package solves a system of nonlinear equations using a Broyden or a Newton method with a choice of global strategies such as line search and trust region. There are options for using a numerical or user supplied Jacobian, for specifying a banded numerical Jacobian and for allowing a singular or ill-conditioned Jacobian.

This package provides a small subset of Unicode symbols, that are useful when building command line applications. They fall back to alternatives on terminals that do not support Unicode.

This package provides analytic derivatives and information matrices for fitted linear mixed effects (lme) models and generalized least squares (gls) models estimated using lme() (from package nlme) and gls() (from package nlme), respectively. The package includes functions for estimating the sampling variance-covariance of variance component parameters using the inverse Fisher information. The variance components include the parameters of the random effects structure (for lme models), the variance structure, and the correlation structure. The expected and average forms of the Fisher information matrix are used in the calculations, and models estimated by full maximum likelihood or restricted maximum likelihood are supported. The package also includes a function for estimating standardized mean difference effect sizes based on fitted lme or gls models.

This package provides methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions.

Read in activity measurements from standard file formats used by circadian rhythm researchers, currently only ClockLab format, and process and plot the data. The central type of plot is the actogram, as first described in "Activity and distribution of certain wild mice in relation to biotic communities" by MS Johnson (1926) doi:10.2307/1373575.

This package provides a set of fonts. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.

This package provides multiple sources of stopwords, for use in text analysis and natural language processing.

This package provides tools to generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.

This package creates alluvial diagrams (also known as parallel sets plots) for multivariate and time series-like data.

This package implements faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create the histogram with fitted probability density function or probability mass function curve, create the body mass index groups, assess the linearity assumption in logistic regression).

This is an extension of the testthat package that lets you add parameters to your unit tests. Parameterized unit tests are often easier to read and more reliable, since they follow the DNRY (do not repeat yourself) rule.

The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption, gzip compression, authentication, and other libcurl goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces.

This package provides non-parametric (and semi-parametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types.

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