This package provides an interface to Amazon Web Services management and governance services, including CloudWatch application and infrastructure monitoring, Auto Scaling for automatically scaling resources, and more.

This package implements targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality.

This package contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.

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.

This package provides a dependency manager for R projects that allows you to manage the R packages your project depends on in an isolated, portable, and reproducible way.

This package provides a set of signal processing functions originally written for Matlab and GNU Octave. It includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.

This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e., a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

This package provides a Shiny app that can disconnect for a variety of reasons: an unrecoverable error occurred in the app, the server went down, the user lost internet connection, or any other reason that might cause the Shiny app to lose connection to its server. With shinydisconnect, you can call disonnectMessage anywhere in a Shiny app's UI to add a nice message when this happens. It works locally (running Shiny apps within RStudio) and on Shiny servers.

This package provides a set of tools for the statistical analysis of data using:

1. normal linear models;

2. generalized linear models;

3. negative binomial regression models as alternative to the Poisson regression models under the presence of overdispersion;

4. beta-binomial and random-clumped binomial regression models as alternative to the binomial regression models under the presence of overdispersion;

5. zero-inflated and zero-altered regression models to deal with zero-excess in count data;

6. generalized nonlinear models;

7. generalized estimating equations for cluster correlated data.

This package enables you to define a command-line interface by just giving it a description in the specific format.

This package provides an implementation of the Anderson-Darling GoF test with p-value calculation based on Marsaglia's 2004 paper "Evaluating the Anderson-Darling Distribution".

This package provides an implementation of efficient approximate leave-one-out (LOO) cross-validation for Bayesian models fit using Markov chain Monte Carlo, as described in doi:10.1007/s11222-016-9696-4. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

This package provides more controls on the option values such as validation and filtering on the values, making options invisible or private.

This package provides utilities to work with indices of effect size and standardized parameters for a wide variety of models, allowing computation and conversion of indices such as Cohen's d, r, odds, etc.

This package provides a simple and intuitive pipe-friendly framework, coherent with the tidyverse design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.

The R package data.table is an extension of data.frame providing functions for fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group, column listing and fast file reading.

This package provides a %<-% operator to perform multiple, unpacking, and destructuring assignment in R. The operator unpacks the right-hand side of an assignment into multiple values and assigns these values to variables on the left-hand side of the assignment.

This package provides common functionality for the dynverse packages. dynverse is created to support the development, execution, and benchmarking of trajectory inference methods.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

This package provides support for measurement units in R vectors, matrices and arrays: automatic propagation, conversion, derivation and simplification of units; raising errors in case of unit incompatibility. It is compatible with the POSIXct, Date and difftime classes.

This package provides Map, Reduce and Filter variants to generate jobs on batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid Engine. Multicore and SSH systems are also supported.

This package provides methods for variable selection for AFT models.