Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation.

The idea is to provide a standard interface to users who use both R and Python for building machine learning models. This package provides a scikit-learn's fit, predict interface to train machine learning models in R.

This package provides functions to speed up work flow for hydrological analysis. Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

This package provides a tool for cutting data into intervals. Allows singleton intervals. Always includes the whole range of data by default. Flexible labelling. Convenience functions for cutting by quantiles etc. Handles dates, times, units and other vectors.

Draws tornado plots for model sensitivity to univariate changes. Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package. Also draws variable importance plots.

This package provides a wrapper for the TexTra API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, a web service for translating texts between different languages. TexTra API account is required to use the service.

This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence.

This package provides functions for handling data from Bioconductor Affymetrix annotation data packages. It produces compact HTML and text reports including experimental data and URL links to many online databases. It allows searching of biological metadata using various criteria.

This is a package providing efficient operations for single cell ATAC-seq fragments and RNA counts matrices. It is interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.

This package provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. It uses a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based on a continuous-time birth-death process.

This package interacts with a suite of web services for chemical information. Sources include: Alan Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver, ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet, NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata.

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.

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 contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.

Functions to help implement the extraction / subsetting / indexing function [ and replacement function [<- of custom matrix-like types (based on S3, S4, etc.), modeled as closely to the base matrix class as possible (with tests to prove it).

The main function biclust() provides several algorithms to find biclusters in two-dimensional data, spectral, plaid model, xmotifs, and bimax. In addition, the package provides methods for data preprocessing (normalization and discretization), visualization, and validation of bicluster solutions.

This package implements the Differential Evolution algorithm. This algorithm is used for the global optimization of a real-valued function of a real-valued parameter vector. The implementation of DifferentialEvolution in DEoptim interfaces with C code for efficiency.

The goal of this package is to generate an attractive and useful website from a source package. pkgdown converts your documentation, vignettes, README file, and more to HTML making it easy to share information about your package online.

Reporting tables often have structure that goes beyond simple rectangular data. The rtables package provides a framework for declaring complex multi-level tabulations and then applying them to data. This framework models both tabulation and the resulting tables as hierarchical, tree-like objects which support sibling sub-tables, arbitrary splitting or grouping of data in row and column dimensions, cells containing multiple values, and the concept of contextual summary computations. A convenient pipe-able interface is provided for declaring table layouts and the corresponding computations, and then applying them to data.

This package implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) <doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (see Lin (2019) <doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) <doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.

User-friendly interface utilities for MCMC models via Just Another Gibbs Sampler (JAGS), facilitating the use of parallel (or distributed) processors for multiple chains, automated control of convergence and sample length diagnostics, and evaluation of the performance of a model using drop-k validation or against simulated data. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. A JAGS extension module provides additional distributions including the Pareto family of distributions, the DuMouchel prior and the half-Cauchy prior.

Search, composite, and download Google Earth Engine imagery with reticulate bindings for the Python module geedim by Dugal Harris. Read the geedim documentation here: <https://geedim.readthedocs.io/>. Wrapper functions are provided to make it more convenient to use geedim to download images larger than the Google Earth Engine size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>. By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.

Simulate multivariate data with arbitrary marginal distributions. bigsimr is a package for simulating high-dimensional multivariate data with a target correlation and arbitrary marginal distributions via Gaussian copula. It utilizes the Julia package Bigsimr.jl for its core routines.