This package provides functions to fit geostatistical data. The data can be continuous, binary or count data and the models implemented are flexible. Conjugate priors are assumed on some parameters while inference on the other parameters can be done through a full Bayesian analysis of by empirical Bayes methods.

The goal of GHCNr is to provide a fast and friendly interface with the Global Historical Climatology Network daily (GHCNd) database, which contains daily summaries of weather station data worldwide (<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>). GHCNd is accessed through the web API <https://www.ncei.noaa.gov/access/services/data/v1>. GHCNr main functionalities consist of downloading data from GHCNd, filter it, and to aggregate it at monthly and annual scales.

Renders LaTeX math equations as native R grid graphics objects (grobs) using the MicroTeX C++ library as the layout engine. Produces resolution-independent vector output that works on any R graphics device, with no external LaTeX installation required. Markdown labels and block documents that mix prose formatting with math are also rendered, for use with both grid and ggplot2'.

This package provides ggplot2 extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom statistical transformations and geometries to visualize data distributions with an emphasis on skewness.

This package provides a reproducible pipeline to conduct genome-wide association studies (GWAS) and extract single-nucleotide polymorphisms (SNPs) for a human trait or disease. Given aggregated GWAS dataset(s) and a user-defined significance threshold, the package retrieves significant SNPs from the GWAS Catalog using supported trait identifiers, annotates their gene context, and can write a harmonised metadata table in comma-separated values (CSV) format, genomic intervals in the Browser Extensible Data (BED) format, and sequences in the FASTA (text-based sequence) format with user-defined flanking regions for clustered regularly interspaced short palindromic repeats (CRISPR) guide design. The existing efo_id argument is retained for backward compatibility. The package prepares computational artifacts for downstream workflows; it does not perform biological causality testing, clinical interpretation, therapeutic design, or wet-lab validation. For details on the resources and methods see: Buniello et al. (2019) <doi:10.1093/nar/gky1120>; Sollis et al. (2023) <doi:10.1093/nar/gkac1010>; Jinek et al. (2012) <doi:10.1126/science.1225829>.

This package provides functions to calculate predicted values and the difference between the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model], polr() [ordinal logistic model], vglm() [generalized ordinal logistic model], multinom() [multinomial model], tobit() [tobit model], svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.

Implementation of routines of the author's PhD thesis on gradient-free Gradient Boosting (Werner, Tino (2020) "Gradient-Free Gradient Boosting", URL <https://oops.uni-oldenburg.de/id/eprint/4290>').

Extensions to Freund and Schapire's AdaBoost algorithm, Y. Freund and R. Schapire (1997) <doi:10.1006/jcss.1997.1504> and Friedman's gradient boosting machine, J.H. Friedman (2001) <doi:10.1214/aos/1013203451>. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMART).

Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R function that evaluates the log un-normalized pdf or pmf. The package also contains a function implementing a specific geometric MH algorithm for performing high-dimensional Bayesian variable selection.

This package provides a workflow for correction of Differential Interferometric Synthetic Aperture Radar (DInSAR) atmospheric delay base on Generic Atmospheric Correction Online Service for InSAR (GACOS) data and correction algorithms proposed by Chen Yu. This package calculate the Both Zenith and LOS direction (User Depend). You have to just download GACOS product on your area and preprocessed D-InSAR unwrapped images. Cite those references and this package in your work, when using this framework. References: Yu, C., N. T. Penna, and Z. Li (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Li, Z., & Penna, N. T. (2017) <doi:10.1016/j.rse.2017.10.038>. Yu, C., Penna, N. T., and Li, Z. (2017) <doi:10.1002/2016JD025753>.

Discrete scales for the colorblind-friendly Okabe-Ito palette, including color', fill', and edge_colour'. ggokabeito provides ggplot2 and ggraph scales to easily use the Okabe-Ito palette in your data visualizations.

This package provides a comprehensive, generalized framework for computing, estimating, and validating Generalized Process Capability Indices (GPCIs) under Hybrid Type-II censored lifetime data. Supports user-supplied probability density or mass functions (PDF/PMF), cumulative distribution functions (CDF), survival functions (SF), and quantile functions. Parameter estimation under Hybrid Type-II censoring is performed via Maximum Likelihood Estimation using the MleCensoR package (Childs et al., 2003 <doi:10.1007/BF02517803>; Balakrishnan & Kundu, 2013 <doi:10.1002/nav.21545>). Computes classical and non-normal capability indices, including Cpy (Maiti et al., 2010 <doi:10.1080/16843703.2010.11673233>), Spmk (Dey & Saha, 2019 <doi:10.1007/s41872-019-00081-4>), CpTk (Saha et al., 2019), Cpc (Saha et al., 2022 <doi:10.1080/02664763.2021.1971632>), CNpmc (Alotaibi et al., 2022 <doi:10.1155/2022/3135264>), CNpmkc (Saha et al., 2024 <doi:10.1142/S021853932450013X>), CNpk (Saha et al., 2018 <doi:10.1080/21681015.2018.1437793>), and Vannman's Cp(u,v) family. Evaluates parametric and non-parametric bootstrap confidence intervals at 90 percent, 95 percent, and 99 percent levels of significance using percentile, normal, basic, BCa, BCp, and studentized bootstrap methods. Computes standard errors, mean squared errors, and coverage probabilities for both distribution parameters and capability indices. Integrates goodness-of-fit testing for Hybrid Type-II censored data via the gofPHCS package.

This package provides a pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) <doi:10.1101/436659> for more information and examples.

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.

This package implements a generalized goodness-of-fit test based on spacings for general progressive Type-II censored data. The test statistic is based on the work of Qin et al. (2022) <doi:10.1080/02664763.2020.1821613> and extends the methodology of Balakrishnan et al. (2003) <doi:10.1007/978-1-4612-0103-8_8>. Users can test data against any distribution by providing custom pdf, cdf, and survival functions. The package supports both normal approximation and Monte Carlo simulation approaches for computing p-values and critical values.

Generalized additive model selection via approximate Bayesian inference is provided. Bayesian mixed model-based penalized splines with spike-and-slab-type coefficient prior distributions are used to facilitate fitting and selection. The approximate Bayesian inference engine options are: (1) Markov chain Monte Carlo and (2) mean field variational Bayes. Markov chain Monte Carlo has better Bayesian inferential accuracy, but requires a longer run-time. Mean field variational Bayes is faster, but less accurate. The methodology is described in He and Wand (2024) <doi:10.1007/s10182-023-00490-y>.

This package performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.

This package provides a ggplot2 extension that adds specialised arrow geometry layers. It offers more arrow options than the standard grid arrows that are built-in many line-based geom layers.

This package provides a machine-readable file and image manifest for the research data deposited in EMBL-EBI BioStudies under accession S-BSST3199 (a time-series petri-dish image dataset of Magnaporthe colonies from twelve plates, with associated morphometric analysis outputs produced by metrics-petri 3.0.0). The original research files are not bundled in this R package; they remain hosted by BioStudies. The manifest can be used in image-analysis and plant-pathology workflows, including workflows based on the grayleafspotr software. Related research outputs are documented using their persistent identifiers.

This package provides tools for the analysis of multi-environment agronomic trials, with a specific focus on plant breeding experiments. Implements the Additive Main effects and Multiplicative Interaction (AMMI) model (Gauch, 1992, ISBN:9780444892409) and the Site Regression (SREG) model (Cornelius, 1996, <doi:10.1201/9780367802226>). To ensure reliable results even with outliers or missing data, it includes robust versions of AMMI (Rodrigues et al., 2016, <doi:10.1093/bioinformatics/btv533>) and SREG (Angelini et al., 2022, <doi:10.1080/15427528.2022.2051217>). Furthermore, the package offers advanced imputation techniques for multi-environment data, covering classical methodologies (Arciniegas-Alarcón et al., 2014, <doi:10.2478/bile-2014-0006>) and recently published imputation methods for MET data (Angelini et al., 2024, <doi:10.1007/s10681-024-03344-z>).

This package provides a set of accessible and automated functions to apply statistical models such as Simple Linear Regression (RLS, from the Spanish Regresión Lineal Simple'), Multiple Linear Regression (RLM, from the Spanish Regresión Lineal Múltiple'), Generalized Linear Models (GLM), and time series analysis through Autoregressive Integrated Moving Average (ARIMA) models. Designed to support teaching at the Universidad Autónoma Chapingo, it facilitates results interpretation and assumption validation through automatic graphical diagnostics. Developed as part of an undergraduate thesis at the Universidad Autónoma Chapingo, under the supervision of Dr. Julio César Buendà a Espinoza (thesis advisor), with the participation of the thesis committee: Diego Ernesto Lira González (secretary), Israel Lerma Serna (member), Juan Uriel Avelar Roblero (alternate), and Elisa del Carmen Martà nez Ochoa (alternate). Methods for regression and time series are based on Montgomery et al. (2021, ISBN:978-1119570141) and Box & Jenkins (1970, ISBN:978-0816211043).

This package provides functions to generate and analyze data for psychology experiments based on the General Recognition Theory.

Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.

An interface for fitting generalized additive models (GAMs) and generalized additive mixed models (GAMMs) using the lme4 package as the computational engine, as described in Helwig (2024) <doi:10.3390/stats7010003>. Supports default and formula methods for model specification, additive and tensor product splines for capturing nonlinear effects, and automatic determination of spline type based on the class of each predictor. Includes an S3 plot method for visualizing the (nonlinear) model terms, an S3 predict method for forming predictions from a fit model, and an S3 summary method for conducting significance testing using the Bayesian interpretation of a smoothing spline.