Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.

If f <- function(x)x^2 and g <- function(x)x+1 it is a constant source of annoyance that "f+g" is not defined. Package vfunc allows you to do this, and we have (f+g)(2) returning 5. The other arithmetic operators are similarly implemented. A wide class of coding bugs is eliminated.

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>.

The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.".

Este pacote traduz os seguintes conjuntos de dados: airlines', airports', ames_raw', AwardsManagers', babynames', Batting', diamonds', faithful', fueleconomy', Fielding', flights', gapminder', gss_cat', iris', Managers', mpg', mtcars', atmos', penguins', People, Pitching', pixarfilms','planes', presidential', table1', table2', table3', table4a', table4b', table5', vehicles', weather', who'. English: It provides a Portuguese translated version of the datasets listed above.

This package provides functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the msm package using age as the time-scale.

Analysis of Bayesian adaptive enrichment clinical trial using Free-Knot Bayesian Model Averaging (FK-BMA) method of Maleyeff et al. (2024) for Gaussian data. Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" <doi:10.1093/biomtc/ujae141>.

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.

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

This package provides optimized C++ code for computing the partial Receiver Operating Characteristic (ROC) test used in niche and species distribution modeling. The implementation follows Peterson et al. (2008) <doi:10.1016/j.ecolmodel.2007.11.008>. Parallelization via OpenMP was implemented with assistance from the DeepSeek Artificial Intelligence Assistant (<https://www.deepseek.com/>).

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.

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.

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.

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.

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>.

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.