Graphical visualization tools for analyzing the data produced by irace'. The iraceplot package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of irace and their performance. The functions just require the log file generated by irace and, in some cases, they can be used with user-provided data.

An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighted (IPW) estimation of average treatment effect (ATE) with misclassified binary outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method. Misclassification probability given a true value of the outcome is assumed to be the same for all individuals.

Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators, following the method developed by Y. Baraud, C. Giraud and S. Huet (2014) <doi:10.1214/13-AIHP539>. In particular it solves the problem of variable selection by choosing the best predictor among predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.

This package provides tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).

Perform a mail merge (mass email) using the message defined in markdown, the recipients in a csv file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the yaml header to specify the subject line of the email. Any braces in the email will be encoded with glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using gmailr'.

This package provides a simple R interface to the OPUS Miner algorithm (implemented in C++) for finding the top-k productive, non-redundant itemsets from transaction data. The OPUS Miner algorithm uses the OPUS search algorithm to efficiently discover the key associations in transaction data, in the form of self-sufficient itemsets, using either leverage or lift. See <http://i.giwebb.com/index.php/research/association-discovery/> for more information in relation to the OPUS Miner algorithm.

Simplifies the manufacturing, analysis and display of pressure volume and leaf drying curves. From the progression of the curves turgor loss point, osmotic potential, apoplastic fraction as well as minimum conductance and stomatal closure can be derived. Methods adapted from Bartlett, Scoffoni, Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x> and Sack, Scoffoni, PrometheusWikiContributors (2011) <http://prometheuswiki.org/tiki-index.php?page=Minimum+epidermal+conductance+%28gmin%2C+a.k.a.+cuticular+conductance%29>.

Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. TDApplied provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.

This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments.

Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets.

PAIRADISE is a method for detecting allele-specific alternative splicing (ASAS) from RNA-seq data. Unlike conventional approaches that detect ASAS events one sample at a time, PAIRADISE aggregates ASAS signals across multiple individuals in a population. By treating the two alleles of an individual as paired, and multiple individuals sharing a heterozygous SNP as replicates, PAIRADISE formulates ASAS detection as a statistical problem for identifying differential alternative splicing from RNA-seq data with paired replicates.

Tools to clean and process text. Tools are geared at checking for substrings that are not optimal for analysis and replacing or removing them (normalizing) with more analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards (2001) doi:10.1006/csla.2001.0169) or extracting them into new variables. For example, emoticons are often used in text but not always easily handled by analysis algorithms. The replace_emoticon() function replaces emoticons with word equivalents.

This package provides an R interface for using AmCharts Library. Based on htmlwidgets', it provides a global architecture to generate JavaScript source code for charts. Most of classes in the library have their equivalent in R with S4 classes; for those classes, not all properties have been referenced but can easily be added in the constructors. Complex properties (e.g. JavaScript object) can be passed as named list. See examples at <https://datastorm-open.github.io/introduction_ramcharts/> and <https://www.amcharts.com/> for more information about the library. The package includes the free version of AmCharts Library. Its only limitation is a small link to the web site displayed on your charts. If you enjoy this library, do not hesitate to refer to this page <https://www.amcharts.com/online-store/> to purchase a licence, and thus support its creators and get a period of Priority Support. See also <https://www.amcharts.com/about/> for more information about AmCharts company.

This package provides a set of functions for organising and analysing datasets from experiments run using Eyelink eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary.

An implementation of the fair data adaptation with quantile preservation described in Plecko & Meinshausen (JMLR 2020, 21(242), 1-44). The adaptation procedure uses the specified causal graph to pre-process the given training and testing data in such a way to remove the bias caused by the protected attribute. The procedure uses tree ensembles for quantile regression. Instructions for using the methods are further elaborated in the corresponding JSS manuscript, see <doi:10.18637/jss.v110.i04>.

An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm themselves.

Tool for import and process data from Lattes curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. getLattes package allows work with Lattes data exported to XML format.

Identifies chromatin interaction modules by constructing a Hi-C contact network based on statistically significant interactions, followed by network clustering. The method enables comparison of module connectivity across two Hi-C datasets and is capable of detecting cell-type-specific regulatory modules. By integrating network analysis with chromatin conformation data, this approach provides insights into the spatial organization of the genome and its functional implications in gene regulation. Author: Sora Yoon (2025) <https://github.com/ysora/HiCociety>.

This package provides tools for the estimation of Heckman selection models with robust variance-covariance matrices. It includes functions for computing the bread and meat matrices, as well as clustered standard errors for generalized Heckman models, see Fernando de Souza Bastos and Wagner Barreto-Souza and Marc G. Genton (2022, ISSN: <https://www.jstor.org/stable/27164235>). The package also offers cluster-robust inference with sandwich estimators, and tools for handling issues related to eigenvalues in covariance matrices.

This package provides tools for parsing NOAA Integrated Surface Data ('ISD') files, described at <https://www.ncdc.noaa.gov/isd>. Data includes for example, wind speed and direction, temperature, cloud data, sea level pressure, and more. Includes data from approximately 35,000 stations worldwide, though best coverage is in North America/Europe/Australia. Data is stored as variable length ASCII character strings, with most fields optional. Included are tools for parsing entire files, or individual lines of data.

This package provides a hybrid of the K-means algorithm and a Majorization-Minimization method to introduce a robust clustering. The reference paper is: Julien Mairal, (2015) <doi:10.1137/140957639>. The two most important functions in package MajKMeans are cluster_km() and cluster_MajKm(). cluster_km() clusters data without Majorization-Minimization and cluster_MajKm() clusters data with Majorization-Minimization method. Both of these functions calculate the sum of squares (SS) of clustering.

Introducing a novel and updated database showcasing Peru's endemic plants. This meticulously compiled and revised botanical collection encompasses a remarkable assemblage of over 7,249 distinct species. The data for this resource was sourced from the work of Govaerts, R., Nic Lughadha, E., Black, N. et al., titled The World Checklist of Vascular Plants: A continuously updated resource for exploring global plant diversity', published in Sci Data 8, 215 (2021) <doi:10.1038/s41597-021-00997-6>.

Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg.