This package implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence. This package is described in Fong et al. (2015) <DOI:10.1093/biostatistics/kxu056>.

This package contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.

This package provides functions for the creation, evaluation and test of decision models based in Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities for a set of indexes, compute utilities and weights for the whole decision tree defining the decision model and simulate weights employing Dirichlet distributions under addition constraints in weights.

This package implements the algorithm in Chen, Wang and Samworth (2020) <arxiv:2003.03668> for online detection of sudden mean changes in a sequence of high-dimensional observations. It also implements methods by Mei (2010) <doi:10.1093/biomet/asq010>, Xie and Siegmund (2013) <doi:10.1214/13-AOS1094> and Chan (2017) <doi:10.1214/17-AOS1546>.

Constraint-based causal discovery using the PC algorithm while accounting for a partial node ordering, for example a partial temporal ordering when the data were collected in different waves of a cohort study. Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395> provide a guide how to use tpc to analyse cohort data.

RTags is a client/server application that indexes C/C++ code and keeps a persistent file-based database of references, declarations, definitions, symbolnames etc. There’s also limited support for ObjC/ObjC++. It allows you to find symbols by name (including nested class and namespace scope). Most importantly we give you proper follow-symbol and find-references support.

This package includes functions to compute the area under the curve of selected measures: the area under the sensitivity curve (AUSEC), the area under the specificity curve (AUSPC), the area under the accuracy curve (AUACC), and the area under the receiver operating characteristic curve (AUROC). The curves can also be visualized. Support for partial areas is provided.

The FAS package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.

Implemented are the Wald-type statistic, a permuted version thereof as well as the ANOVA-type statistic for general factorial designs, even with non-normal error terms and/or heteroscedastic variances, for crossed designs with an arbitrary number of factors and nested designs with up to three factors. Friedrich et al. (2017) <doi:10.18637/jss.v079.c01>.

Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.

Primarily, the qcv package computes key indices related to the Quantifying Construct Validity procedure (QCV; Westen & Rosenthal, 2003 <doi:10.1037/0022-3514.84.3.608>; see also Furr & Heuckeroth, in press). The qcv() function is the heart of the qcv package, but additional functions in the package provide useful ancillary information related to the QCV procedure.

Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.

This package provides functions for point and interval estimation in error-in-variables models via total least squares or generalized total least squares method. See Golub and Van Loan (1980) <doi:10.1137/0717073>, Gleser (1981) <https://www.jstor.org/stable/2240867>, Ivan Markovsky and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for more information.

Quickly create, run, and report structural equation models, and twin models. See ?umx for help, and umx_open_CRAN_page("umx") for NEWS. Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.

This package provides a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines.

DSS is an R library performing differential analysis for count-based sequencing data. It detects differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions.

This package lets you record test suite HTTP requests and replay them during future runs. It works by hooking into the webmockr R package for matching HTTP requests by various rules, and then caching real HTTP responses on disk in cassettes. Subsequent HTTP requests matching any previous requests in the same cassette use a cached HTTP response.

R is a language and environment for statistical computing and graphics. It provides a variety of statistical techniques, such as linear and nonlinear modeling, classical statistical tests, time-series analysis, classification and clustering. It also provides robust support for producing publication-quality data plots. A large amount of 3rd-party packages are available, greatly increasing its breadth and scope.

This package provides functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others. Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.

Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.

This package provides functions to estimate the probability to receive the observed treatment, based on individual characteristics. The inverse of these probabilities can be used as weights when estimating causal effects from observational data via marginal structural models. Both point treatment situations and longitudinal studies can be analysed. The same functions can be used to correct for informative censoring.

Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.

This package implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.