This natural language processing toolkit provides language-agnostic tokenization', parts of speech tagging', lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

This package provides raster grid logic, operations that describe a discretized rectangular domain and do not require access to materialized data. Grids are arrays with dimension and extent, and many operations are functions of dimension only: number of columns, number of rows, or they are a combination of the dimension and the extent the range in x and the range in y in that order. Here we provide direct access to this logic without need for connection to any materialized data or formats. Grid logic includes functions that relate the cell index to row and column, or row and column to cell index, row, column or cell index to position. These methods are described in Loudon, TV, Wheeler, JF, Andrew, KP (1980) <doi:10.1016/0098-3004(80)90015-1>, and implementations were in part derived from Hijmans R (2024) <doi:10.32614/CRAN.package.terra>.

Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens.

Comprehensive implementation of advanced ARDL methodologies for cointegration analysis with structural breaks and asymmetric effects. Includes: (1) Fourier Quantile ARDL (FQARDL) - quantile regression with Fourier approximation for analyzing relationships across the conditional distribution; (2) Fourier Nonlinear ARDL (FNARDL) - asymmetric cointegration with partial sum decomposition following Shin, Yu & Greenwood-Nimmo (2014) <doi:10.1007/978-1-4899-8008-3_9>; (3) Multi-Threshold NARDL (MTNARDL) - multiple regime asymmetry analysis; (4) Fourier Unit Root Tests - ADF and KPSS tests with Fourier terms following Enders & Lee (2012) <doi:10.1016/j.econlet.2012.05.019> and Becker, Enders & Lee (2006) <doi:10.1111/j.1467-9892.2006.00490.x>. Features automatic lag and frequency selection, PSS bounds testing following Pesaran, Shin & Smith (2001) <doi:10.1002/jae.616>, bootstrap cointegration tests, Wald tests for asymmetry, dynamic multiplier computation, and publication-ready visualizations. Ported from Stata/Python by Dr. Merwan Roudane.

Learning, manipulation and evaluation of mixtures of truncated basis functions (MoTBFs), which include mixtures of polynomials (MOPs) and mixtures of truncated exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian networks (I. Pérez-Bernabé, A. Salmerón, H. Langseth (2015) <doi:10.1007/978-3-319-20807-7_36>; H. Langseth, T.D. Nielsen, I. Pérez-Bernabé, A. Salmerón (2014) <doi:10.1016/j.ijar.2013.09.012>; I. Pérez-Bernabé, A. Fernández, R. Rumà , A. Salmerón (2016) <doi:10.1007/s10618-015-0429-7>). The package provides functionality for learning univariate, multivariate and conditional densities, with the possibility of incorporating prior knowledge. Structural learning of hybrid Bayesian networks is also provided. A set of useful tools is provided, including plotting, printing and likelihood evaluation. This package makes use of S3 objects, with two new classes called motbf and jointmotbf'.

This natural language processing toolkit provides language-agnostic tokenization, parts of speech tagging, lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at https://universaldependencies.org/format.html. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at doi:10.18653/v1/K17-3009. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

Solves optimal pairing and matching problems using linear assignment algorithms. Provides implementations of the Hungarian method (Kuhn 1955) <doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm (Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling (Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms (Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons.

This package provides a collection of tools and data for analyzing the Gause microcosm experiments, and for fitting Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic growth, and multi-species interaction models, e.g. of competition, predator/prey relationships, or mutualism. See documentation for individual functions for examples. In general, see the lv_optim() function for examples of how to fit parameter values in multi-species systems. Note that the general methods applied here, as well as the form of the differential equations that we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., available at <http://hdl.handle.net/11299/204551>, and in Lina K. Mühlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. gauseR': Simple methods for fitting Lotka-Volterra models describing Gause's Struggle for Existence in the journal Ecology and Evolution.

This package provides a collection of clinical trial designs and methods, implemented in rstan and R, including: the Continual Reassessment Method by O'Quigley et al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and more. We provide functions to aid model-fitting and analysis. The rstan implementations may also serve as a cookbook to anyone looking to extend or embellish these models. We hope that this package encourages the use of Bayesian methods in clinical trials. There is a preponderance of early phase trial designs because this is where Bayesian methods are used most. If there is a method you would like implemented, please get in touch.

Simple and efficient access to Yahoo Finance's historical data API <https://finance.yahoo.com/> for querying and retrieval of financial data. The core functionality of the yfhist package abstracts the complexities of interacting with Yahoo Finance APIs, such as session management, crumb and cookie handling, query construction, date validation, and interval management. This abstraction allows users to focus on retrieving data rather than managing API details. Use cases include historical data across a range of security types including equities & ETFs, indices, and other tickers. The package supports flexible query capabilities, including customizable date ranges, multiple time intervals, and automatic data validation. It automatically manages interval-specific limitations, such as lookback periods for intraday data and maximum date ranges for minute-level intervals. The implementation leverages standard HTTP libraries to handle API interactions efficiently and provides support for both R and Python to ensure accessibility for a broad audience.

This package provides a simple approach to using a probit or logit analysis to calculate lethal concentration (LC) or time (LT) and the appropriate fiducial confidence limits desired for selected LC or LT for ecotoxicology studies (Finney 1971; Wheeler et al. 2006; Robertson et al. 2007). The simplicity of ecotox comes from the syntax it implies within its functions which are similar to functions like glm() and lm(). In addition to the simplicity of the syntax, a comprehensive data frame is produced which gives the user a predicted LC or LT value for the desired level and a suite of important parameters such as fiducial confidence limits and slope. Finney, D.J. (1971, ISBN: 052108041X); Wheeler, M.W., Park, R.M., and Bailer, A.J. (2006) <doi:10.1897/05-320R.1>; Robertson, J.L., Savin, N.E., Russell, R.M., and Preisler, H.K. (2007, ISBN: 0849323312).

This package provides a comprehensive, modular framework for computing the Soil Quality Index (SQI) using six established methods: Linear Scoring (Doran and Parkin, 1994, <doi:10.2136/sssaspecpub35.c1>), Regression-based (Masto et al., 2008, <doi:10.1007/s10661-007-9697-z>), Principal Component Analysis-based (Andrews et al., 2004, <doi:10.2136/sssaj2004.1945>), Fuzzy Logic, Entropy Weighting (Shannon, 1948, <doi:10.1002/j.1538-7305.1948.tb01338.x>), and TOPSIS (Hwang and Yoon, 1981, <doi:10.1007/978-3-642-48318-9>). Implements four variable scoring functions: more-is-better, less-is-better, optimum-value, and trapezoidal, following Karlen and Stott (1994, <doi:10.2136/sssaspecpub35.c4>). Includes automated Minimum Data Set selection via Principal Component Analysis with Variance Inflation Factor filtering (Kaiser, 1960, <doi:10.1177/001316446002000116>), one-way ANOVA with Tukey HSD post-hoc tests, leave-one-out sensitivity analysis, and publication-quality visualization using ggplot2'.

The heterogeneous treatment effect estimation procedure proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>. The proposed method is applicable, for example, when selecting a small number of most (or least) efficacious treatments from a large number of alternative treatments as well as when identifying subsets of the population who benefit (or are harmed by) a treatment of interest. The method adapts the Support Vector Machine classifier by placing separate LASSO constraints over the pre-treatment parameters and causal heterogeneity parameters of interest. This allows for the qualitative distinction between causal and other parameters, thereby making the variable selection suitable for the exploration of causal heterogeneity. The package also contains a class of functions, CausalANOVA, which estimates the average marginal interaction effects (AMIEs) by a regularized ANOVA as proposed by Egami and Imai (2019). It contains a variety of regularization techniques to facilitate analysis of large factorial experiments.

The Gene Ontology (GO) Consortium <https://geneontology.org/> organizes genes into hierarchical categories based on biological process (BP), molecular function (MF) and cellular component (CC, i.e., subcellular localization). Tools such as GoMiner (see Zeeberg, B.R., Feng, W., Wang, G. et al. (2003) <doi:10.1186/gb-2003-4-4-r28>) can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Microarray studies are usually analyzed with BP, whereas proteomics researchers often prefer CC. To capture the benefit of both of those ontologies, I developed a two-dimensional version of High-Throughput GoMiner ('HTGM2D'). I generate a 2D heat map whose axes are any two of BP, MF, or CC, and the value within a picture element of the heat map reflects the Jaccard metric p-value for the number of genes in common for the corresponding pair.

This package creates realistic random trajectories in a 3-D space between two given fix points, so-called conditional empirical random walks (CERWs). The trajectory generation is based on empirical distribution functions extracted from observed trajectories (training data) and thus reflects the geometrical movement characteristics of the mover. A digital elevation model (DEM), representing the Earth's surface, and a background layer of probabilities (e.g. food sources, uplift potential, waterbodies, etc.) can be used to influence the trajectories. Unterfinger M (2018). "3-D Trajectory Simulation in Movement Ecology: Conditional Empirical Random Walk". Master's thesis, University of Zurich. <https://www.geo.uzh.ch/dam/jcr:6194e41e-055c-4635-9807-53c5a54a3be7/MasterThesis_Unterfinger_2018.pdf>. Technitis G, Weibel R, Kranstauber B, Safi K (2016). "An algorithm for empirically informed random trajectory generation between two endpoints". GIScience 2016: Ninth International Conference on Geographic Information Science, 9, online. <doi:10.5167/uzh-130652>.

This package provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) <doi:10.1037/0033-2909.86.2.420>, and contextual effect decomposition via the Mundlak (1978) <doi:10.2307/1913646> specification distinguishing within- and between-cluster components. Supports visualization of random slopes and cross-level interactions following Hofmann and Gavin (1998) <doi:10.1177/014920639802400504> and Hamaker and Muthen (2020) <doi:10.1037/met0000239>. Multilevel models are estimated using lme4 (Bates et al., 2015 <doi:10.18637/jss.v067.i01>). An optional Shiny application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049).

This package provides a toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.

This package provides a weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

This package provides a workflow for the use of EaSIeR tool, developed to assess patients likelihood to respond to ICB therapies providing just the patients RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy.

This package performs iterative extrapolation of species haplotype accumulation curves using a nonparametric stochastic (Monte Carlo) optimization method for assessment of specimen sampling completeness based on the approach of Phillips et al. (2015) <doi:10.1515/dna-2015-0008>, Phillips et al. (2019) <doi:10.1002/ece3.4757> and Phillips et al. (2020) <doi: 10.7717/peerj-cs.243>. HACSim outputs a number of useful summary statistics of sampling coverage ("Measures of Sampling Closeness"), including an estimate of the likely required sample size (along with desired level confidence intervals) necessary to recover a given number/proportion of observed unique species haplotypes. Any genomic marker can be targeted to assess likely required specimen sample sizes for genetic diversity assessment. The method is particularly well-suited to assess sampling sufficiency for DNA barcoding initiatives. Users can also simulate their own DNA sequences according to various models of nucleotide substitution. A Shiny app is also available.

Incorporates Approximate Bayesian Computation to get a posterior distribution and to select a model optimal parameter for an observation point. Additionally, the meta-sampling heuristic algorithm is realized for parameter estimation, which requires no model runs and is dimension-independent. A sampling scheme is also presented that allows model runs and uses the meta-sampling for point generation. A predictor is realized as the meta-sampling for the model output. All the algorithms leverage a machine learning method utilizing the maxima weighted Isolation Kernel approach, or MaxWiK'. The method involves transforming raw data to a Hilbert space (mapping) and measuring the similarity between simulated points and the maxima weighted Isolation Kernel mapping corresponding to the observation point. Comprehensive details of the methodology can be found in the papers Iurii Nagornov (2024) <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023) <doi:10.1007/978-3-031-29168-5_18>.

Pathway Analysis is statistically linking observations on the molecular level to biological processes or pathways on the systems(i.e., organism, organ, tissue, cell) level. Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative. However, this can lead to identifying spurious pathways as statistically significant since components are often shared amongst pathways. SIGORA seeks to avoid this pitfall by focusing on genes or gene pairs that are (as a combination) specific to a single pathway. In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways. The current version allows for pathway analysis of human and mouse datasets. In addition, it contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories and mechanisms for extracting GPS for user-supplied repositories.

We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal.

This package provides a high performance interface to the Global Biodiversity Information Facility, GBIF'. In contrast to rgbif', which can access small subsets of GBIF data through web-based queries to a central server, gbifdb provides enhanced performance for R users performing large-scale analyses on servers and cloud computing providers, providing full support for arbitrary SQL or dplyr operations on the complete GBIF data tables (now over 1 billion records, and over a terabyte in size). gbifdb accesses a copy of the GBIF data in parquet format, which is already readily available in commercial computing clouds such as the Amazon Open Data portal and the Microsoft Planetary Computer, or can be accessed directly without downloading, or downloaded to any server with suitable bandwidth and storage space. The high-performance techniques for local and remote access are described in <https://duckdb.org/why_duckdb> and <https://arrow.apache.org/docs/r/articles/fs.html> respectively.