Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.

Apply an adaptation of the SuperFastHash algorithm to any R object. Hash whole R objects or, for vectors or lists, hash R objects to obtain a set of hash values that is stored in a structure equivalent to the input. See <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash algorithm.

This package implements an efficient algorithm to fit and tune penalized Support Vector Machine models using the generalized coordinate descent algorithm. Designed to handle high-dimensional datasets effectively, with emphasis on precision and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

These are data and functions to support quantitative peace science research. The data are important state-year information on democracy and wealth, which require periodic updates and regular maintenance. The functions permit some exploratory and diagnostic assessment of the kinds of data in demand by the community, but do not impose many dependencies on the user.

Multiple imputation using XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. It yields smaller variances than the standard bias correction, the generalised regression estimator.

This package provides tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.

This package provides tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.

Permutation based non-parametric analysis of CRISPR screen data. Details about this algorithm are published in the following paper published on BMC genomics, Jia et al. (2017) <doi:10.1186/s12864-017-3938-5>: A permutation-based non-parametric analysis of CRISPR screen data. Please cite this paper if you use this algorithm for your paper.

The spork syntax describes label formatting concisely, supporting mixed nesting of subscripts and superscripts to arbitrary depth. It intends to be easy to read and write in plain text, and easy to convert to equivalent presentations in plotmath', latex', and html'. Greek symbols and a multiplication symbol are explicitly supported. See ?as_spork and ?as_previews.

Interval fusion and selection procedures for regression with functional inputs. Methods include a semiparametric approach based on Sliced Inverse Regression (SIR), as described in <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are also included in the package) and a random forest based approach, as described in <doi:10.1002/sam.11705>.

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

Analysis Results Standard (ARS), a foundational standard by CDISC (Clinical Data Interchange Standards Consortium), provides a logical data model for metadata describing all components to calculate Analysis Results. <https://www.cdisc.org/standards/foundational/analysis-results-standard> Using siera package, ARS metadata is ingested (JSON or Excel format), producing programmes to generate Analysis Results Datasets (ARDs).

This package creates images that are the proper size for social media. Beautiful plots, charts and graphs wither and die if they are not shared. Social media is perfect for this but every platform has its own image dimensions. With smpic you can easily save your plots with the exact dimensions needed for the different platforms.

This package creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.

Package of wrapper functions using R6 class to send requests to Microsoft Teams <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks. When you need to share information or data from R to Teams', rather than copying/pasting, you can use this package to send well-formatted output from multiple R objects.

Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations.

The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis.

The package provides `rlang` data masks for the SummarizedExperiment class. The enables the evaluation of unquoted expression in different contexts of the SummarizedExperiment object with optional access to other contexts. The goal for `plyxp` is for evaluation to feel like a data.frame object without ever needing to unwind to a rectangular data.frame.

This R package is for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis, such as differential gene expression or transcript usage.

This package provides methods for spatial data analysis, especially raster data. The included methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported.

This package performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.

An interface to the ArcGIS arcpy and arcgis python API <https://pro.arcgis.com/en/pro-app/latest/arcpy/get-started/arcgis-api-for-python.htm>. Provides various tools for installing and configuring a Conda environment for accessing ArcGIS geoprocessing functions. Helper functions for manipulating and converting ArcGIS objects from R are also provided.