Differentiate client errors (4xx) from server errors (5xx) for the plumber and RestRserve HTTP API frameworks. The package also includes a built-in logging mechanism to standard output (STDOUT) or standard error (STDERR) depending on the log level.

This package provides tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.

Recursive partytioning of transformation models with corresponding random forest for conditional transformation models as described in Transformation Forests (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) and Top-Down Transformation Choice (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).

This package provides a Bayesian companion to the rms package, rmsb provides Bayesian model fitting, post-fit estimation, and graphics. It implements Bayesian regression models whose fit objects can be processed by rms functions such as contrast()', summary()', Predict()', nomogram()', and latex()'. The fitting function currently implemented in the package is blrm() for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) <https://www.jstor.org/stable/2347760>.

This package provides a classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.

Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group). Main goal is to leverage R capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions. In addition, continuously improve and enhance Riex by applying best practices and being in tune with users feedback and requirements. Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using Riex (<https://iexcloud.io/terms/>).

This package provides a parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less pretty output than a vendor specific parser.

This package provides an SCSS compiler, powered by the libsass library. With this, R developers can use variables, inheritance, and functions to generate dynamic style sheets. The package uses the Sass CSS extension language, which is stable, powerful, and CSS compatible.

This package provides fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen glue.

Recoll finds documents based on their contents as well as their file names. It can search most document formats, but you may need external applications for text extraction. It can reach any storage place: files, archive members, email attachments, transparently handling decompression.

This package implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level.

SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions.

The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis.

Parentage assignment package. Parentage assignment is performed based on observed average Mendelian transmission probability distributions or Exclusion. The main functions of this package are the function APIS_2n(), APIS_3n() and launch_APIShiny(), which perform parentage assignment.

This package provides a collection of command-line color styles based on the crayon package. Colt styles are defined in themes that can easily be switched, to ensure command line output looks nice on dark as well as light consoles.

Estimation and inference for linear models where some or all of the fixed-effects coefficients are subject to order restrictions. This package uses the robust residual bootstrap methodology for inference, and can handle some structure in the residual variance matrix.

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.

Unified regularized estimating equation solver. Currently the package includes one solver with the l1 penalty only. More solvers and penalties are under development. Reference: Yi Yang, Yuwen Gu, Yue Zhao, Jun Fan (2021) <doi:10.48550/arXiv.2110.11074>.

Estimation of covariance matrices as solutions of continuous time Lyapunov equations. Sparse coefficient matrix and diagonal noise are estimated with a proximal gradient method for an l1-penalized loss minimization problem. Varando G, Hansen NR (2020) <arXiv:2005.10483>.

Given an adjacency matrix drawn from a Generalized Stochastic Block Model with missing observations, this package robustly estimates the probabilities of connection between nodes and detects outliers nodes, as describes in Gaucher, Klopp and Robin (2019) <arXiv:1911.13122>.

Up-to-date data from the Unicode CLDR Project (where CLDR stands for Common Locale Data Repository') are available here as a series of easy-to-parse datasets. Several functions are provided for extracting key elements from the tabular datasets.