DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) <doi:10.18637/jss.v059.i05>, Turner, S. D. (2018) <doi:10.21105/joss.00731>, Rosseel, D. (2012) <doi:10.18637/jss.v048.i02>.

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://theses.hal.science/tel-01943718>.

Inference of chromosome-length haplotypes using a few haploid gametes of an individual. The gamete genotype data may be generated from various platforms including genotyping arrays and sequencing even with low-coverage. Hapi simply takes genotype data of known hetSNPs in single gamete cells as input and report the high-resolution haplotypes as well as confidence of each phased hetSNPs. The package also includes a module allowing downstream analyses and visualization of identified crossovers in the gametes.

Variable selection for generalized linear models and the Cox proportional-hazards model in ultrahigh dimensions via the iterated block Gibbs sampler (IBGS). The sampler is implemented in C with parallel block screening through OpenMP', and supports the gaussian, binomial and poisson families (fitted by least squares or iteratively reweighted least squares) as well as the Cox model for survival analysis (fitted by its Efron partial likelihood), together with the AIC, BIC, AICc and extended BIC model selection criteria.

This package provides a SAS interface, through SASPy'(<https://sassoftware.github.io/saspy/>) and reticulate'(<https://rstudio.github.io/reticulate/>). This package helps you create SAS sessions, execute SAS code in remote SAS servers, retrieve execution results and log, and exchange datasets between SAS and R'. It also helps you to install SASPy and create a configuration file for the connection. Please review the SASPy license file as instructed so that you comply with its separate and independent license.

This package provides tools for predicting ICU length of stay and assessing ICU efficiency. It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)<https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)<https://pubmed.ncbi.nlm.nih.gov/37922007/>. More information: <https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.

Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping introduced by Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>, following a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples.

The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.

An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed.

Query the four endpoints of the Air and Water Database (AWDB) REST API maintained by the National Water and Climate Center (NWCC) at the United States Department of Agriculture (USDA). Endpoints include data, forecast, reference-data, and metadata. The package is extremely light weight, with Rust via extendr doing most of the heavy lifting to deserialize and flatten deeply nested JSON responses. The AWDB can be found at <https://wcc.sc.egov.usda.gov/awdbRestApi/swagger-ui/index.html>.

Compute expected shortfall (ES) and Value at Risk (VaR) from a quantile function, distribution function, random number generator, probability density function, or data. ES is also known as Conditional Value at Risk (CVaR). Virtually any continuous distribution can be specified. The functions are vectorized over the arguments. The computations are done directly from the definitions, see e.g. Acerbi and Tasche (2002) <doi:10.1111/1468-0300.00091>. Some support for GARCH models is provided, as well.

This package implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) <doi:10.48550/arXiv.2108.03336> for details.

Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.

Calculate the ratio of iron oxides, hematite and goethite, in soil using the diffuse reflectance technique. The Kubelka-Munk theory, second derivative analysis, and spectral region amplitudes related to hematite and goethite content are used for quantification (Torrent, J., & Barron, V. (2008) <doi:10.2136/sssabookser5.5.c13>). Additionally, the package calculates soil color in the visible spectrum using Munsell and RGB color spaces, based on color theory (Viscarra et al. (2006) <doi:10.1016/j.geoderma.2005.07.017>).

This package provides access to the Philippine Standard Geographic Code (PSGC), an official classification system for geographic areas in the Philippines published by the Philippine Statistics Authority (PSA). Includes area names, geographic levels (Region, Province, City, Municipality, Sub-Municipality, and Barangay), and census population figures across multiple PSA publication releases. Offers utilities to look up individual codes, filter by geographic level, track code changes across releases via a built-in crosswalk, and retrieve population data in long or wide format.

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.

Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a path of candidate models from which model selection can be performed. This slow brewing approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.

Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.

Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data and penalized estimating equations for generalized linear models with multiple imputation. Reference: Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized estimating equations for generalized linear models with multiple imputation", <doi:10.1214/22-AOAS1721>. Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data", <doi:10.1093/jrsssc/qlad028>.

This package provides an object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided.

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.