An API client for NASA POWER global meteorology, surface solar energy and climatology data API. POWER (Prediction Of Worldwide Energy Resources) data are freely available for download with varying spatial resolutions dependent on the original data and with several temporal resolutions depending on the POWER parameter and community. This work is funded through the NASA Earth Science Directorate Applied Science Program. For more on the data themselves, the methodologies used in creating, a web-based data viewer and web access, please see <https://power.larc.nasa.gov/>.

Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 <doi:10.1128/AEM.00062-07>. The package also provides functions to read in FASTA'-formatted sequence data.

This package implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. It deals correctly with parameters whose estimated values can be considered as being negative infinity. It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.

This package provides tools for the analysis of reverse-phase protein arrays (RPPAs), which are also known as tissue lysate arrays or simply lysate arrays'. The package's primary purpose is to input a set of quantification files representing dilution series of samples and control points taken from scanned RPPA slides and determine a relative log concentration value for each valid dilution series present in each slide and provide graphical visualization of the input and output data and their relationships. Other optional features include generation of quality control scores for judging the quality of the input data, spatial adjustment of sample points based on controls added to the slides, and various types of normalization of calculated values across a set of slides. The package was derived from a previous package named SuperCurve. For a detailed description of data inputs and outputs, usage information, and a list of related papers describing methods used in the package please review the vignette Guide_to_RPPASPACE'. RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data'. Bioinformatics Nov 15;38(22):5131-5133. <doi: 10.1093/bioinformatics/btac665>.

Dunn's test computes stochastic dominance & reports pairwise comparisons. This is done following a Kruskal-Wallis test (Kruskal and Wallis, 1952). It employs Dunn's z-test-statistic approximations for rank statistics, conducting k(k-1)/2 comparisons. The null hypothesis assumes that the probability of a randomly selected value from the first group being larger than one from the second group is one half, similar to the Wilcoxon-Mann-Whitney test. Dunn's test serves as a test for median difference and takes into account tied ranks.

The software formalises a framework for classification and survival model evaluation in R. There are four stages; Data transformation, feature selection, model training, and prediction. The requirements of variable types and variable order are fixed, but specialised variables for functions can also be provided. The framework is wrapped in a driver loop that reproducibly carries out a number of cross-validation schemes. Functions for differential mean, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework.

DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values.

Paquete creado con el fin de facilitar el cálculo y distribución del à ndice Socio Material Territorial (ISMT), elaborado por el Observatorio de Ciudades UC. La metodologà a completa está disponible en "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)]. || Package created to facilitate the calculation and distribution of the Socio-Material Territorial Index by Observatorio de Ciudades UC. The full methodology is available at "ISMT" (<https://ideocuc-ocuc.hub.arcgis.com/datasets/6ed956450cfc4293b7d90df3ce3474e4/about>) [Observatorio de Ciudades UC (2019)].

The function get_parameters() is intended to be used within a docker container to read keyword arguments from a .json file automagically. A tool.yaml file contains specifications on these keyword arguments, which are then passed as input to containerized R tools in the [tool-runner framework](<https://github.com/hydrocode-de/tool-runner>). A template for a containerized R tool, which can be used as a basis for developing new tools, is available at the following URL: <https://github.com/VForWaTer/tool_template_r>.

Conducts a cointegration test for high-dimensional vector autoregressions (VARs) of order k based on the large N,T asymptotics of Bykhovskaya and Gorin, 2022 (<doi:10.48550/arXiv.2202.07150>). The implemented test is a modification of the Johansen likelihood ratio test. In the absence of cointegration the test converges to the partial sum of the Airy-1 point process. This package contains simulated quantiles of the first ten partial sums of the Airy-1 point process that are precise up to the first three digits.

Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis.

The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.

This package implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.

This package creates a local Lightning Memory-Mapped Database ('LMDB') of many commonly used taxonomic authorities and provides functions that can quickly query this data. Supported taxonomic authorities include the Integrated Taxonomic Information System ('ITIS'), National Center for Biotechnology Information ('NCBI'), Global Biodiversity Information Facility ('GBIF'), Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). Name and identifier resolution using LMDB can be hundreds of times faster than either relational databases or internet-based queries. Precise data provenance information for data derived from naming providers is also included.

The googleVis package provides an interface between R and the Google Charts API. Google Charts offer interactive charts which can be embedded into web pages. The functions of the googleVis package allow the user to visualise data stored in R data frames with Google Charts without uploading the data to Google. The output of a googleVis function is HTML code that contains the data and references to JavaScript functions hosted by Google. googleVis makes use of the internal R HTTP server to display the output locally.

This package provides an R Client for the Europe PubMed Central RESTful Web Service. It gives access to both metadata on life science literature and open access full texts. Europe PMC indexes all PubMed content and other literature sources including Agricola, a bibliographic database of citations to the agricultural literature, or Biological Patents. In addition to bibliographic metadata, the client allows users to fetch citations and reference lists. Links between life-science literature and other EBI databases, including ENA, PDB or ChEMBL are also accessible.

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Calculates several entropy metrics for spatial data inspired by Boltzmann's entropy formula. It includes metrics introduced by Cushman for landscape mosaics (Cushman (2015) <doi:10.1007/s10980-015-0305-2>), and landscape gradients and point patterns (Cushman (2021) <doi:10.3390/e23121616>); by Zhao and Zhang for landscape mosaics (Zhao and Zhang (2019) <doi:10.1007/s10980-019-00876-x>); and by Gao et al. for landscape gradients (Gao et al. (2018) <doi:10.1111/tgis.12315>; Gao and Li (2019) <doi:10.1007/s10980-019-00854-3>).

Noise in the time-series data significantly affects the accuracy of the Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression are considered here). Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the time series data into sub-series and help to improve the model performance. The models can achieve higher prediction accuracy than the traditional ML models. Two models have been provided here for time series forecasting. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Power analysis is used in the estimation of sample sizes for experimental designs. Most programs and R packages will only output the highest recommended sample size to the user. Often the user input can be complicated and computing multiple power analyses for different treatment comparisons can be time consuming. This package simplifies the user input and allows the user to view all of the sample size recommendations or just the ones they want to see. The calculations used to calculate the recommended sample sizes are from the pwr package.

Please note: active development has moved to packages validate and errorlocate'. Facilitates reading and manipulating (multivariate) data restrictions (edit rules) on numerical and categorical data. Rules can be defined with common R syntax and parsed to an internal (matrix-like format). Rules can be manipulated with variable elimination and value substitution methods, allowing for feasibility checks and more. Data can be tested against the rules and erroneous fields can be found based on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with using the igraph package.

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

The goal of gsDesign2 is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, gsDesign2 offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the gsDesign package is intended for selecting boundaries.

Estimation of joint models for multivariate longitudinal markers (with various distributions available) and survival outcomes (possibly accounting for competing risks) with Integrated Nested Laplace Approximations (INLA). The flexible and user friendly function joint() facilitates the use of the fast and reliable inference technique implemented in the INLA package for joint modeling. More details are given in the help page of the joint() function (accessible via ?joint in the R console) and the vignette associated to the joint() function (accessible via vignette("INLAjoint") in the R console).