Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. <doi:10.1007/s11135-025-02351-z>.

This package provides four variants of three-way correspondence analysis (ca): three-way symmetrical ca, three-way non-symmetrical ca, three-way ordered symmetrical ca and three-way ordered non-symmetrical ca.

Merging data from multiple sources is a relevant approach for comprehensively evaluating complex systems. However, the inherent problems encountered when analyzing single tables are amplified with the generation of multi-block datasets, and finding the relationships between data layers of increasing complexity constitutes a challenging task. For that purpose, a generic methodology is proposed by combining the strength of established data analysis strategies, i.e. multi-block approaches and the Orthogonal Partial Least Squares (OPLS) framework to provide an efficient tool for the fusion of data obtained from multiple sources. The package enables quick and efficient implementation of the consensus OPLS model for any horizontal multi-block data structures (observation-based matching). Moreover, it offers an interesting range of metrics and graphics to help to determine the optimal number of components and check the validity of the model through permutation tests. Interpretation tools include score and loading plots, Variable Importance in Projection (VIP), functionality predict for SHAP computing, and performance coefficients such as R2, Q2, and DQ2 coefficients. J. Boccard and D.N. Rutledge (2013) <doi:10.1016/j.aca.2013.01.022>.

This package performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python spaCy', scispaCy', and medspaCy packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the scispaCy package is described by Neumann (2019) <doi:10.18653/v1/W19-5034>. The medspacy package uses ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy also supports entity embeddings from scispaCy and UMLS cui2vec concept embeddings developed by Beam (2018) <arXiv:1804.01486>.

This package provides a toolkit for computing and visualizing CAPL-2 (Canadian Assessment of Physical Literacy, Second Edition; <https://www.capl-eclp.ca>) scores and interpretations from raw data.

Client for CKAN API (<https://ckan.org/>). Includes interface to CKAN APIs for search, list, show for packages, organizations, and resources. In addition, provides an interface to the datastore API.

This package provides tools for fitting, assessing, and comparing logistic and conditional logistic regression models. Includes residual diagnostics and goodness of fit measures for model development and evaluation in matched case control studies.

Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances. The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.

Enhances the ini package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where %(....)s instances are interpolated by other included options or outside variables.

Retorna detalhes de dados de CEPs brasileiros, bairros, logradouros e tal. (Returns info of Brazilian postal codes, city names, addresses and so on.).

Fitting and inference functions for generalized linear models with constrained coefficients.

This package implements the three-step workflow for robust analysis of change in two repeated measurements of continuous outcomes, described in Ning et al. (in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology.

This package implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry <doi:10.1021/ac901143w> and other normalization algorithms.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

This package implements various estimators for average treatment effects - an inverse probability weighted (IPW) estimator, an augmented inverse probability weighted (AIPW) estimator, and a standard regression estimator - that make use of generalized additive models for the treatment assignment model and/or outcome model. See: Glynn, Adam N. and Kevin M. Quinn. 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator." Political Analysis. 18: 36-56.

Quickly set and summarize contrasts for factors prior to regression analyses. Intended comparisons, baseline conditions, and intercepts can be explicitly set and documented without the user needing to directly manipulate matrices. Reviews and introductions for contrast coding are available in Brehm and Alday (2022)<doi:10.1016/j.jml.2022.104334> and Schad et al. (2020)<doi:10.1016/j.jml.2019.104038>.

Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.

Data personally collected about the spread of COVID-19 (SARS-COV-2) in Tunisia <https://github.com/MounaBelaid/covid19datatunisia>.

This package contains the basic functions to apply the unified framework for partitioning the drivers of stability of ecological communities. Segrestin et al. (2024) <doi:10.1111/geb.13828>.

This package performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test.

Calculate p-values and confidence intervals using cluster-adjusted t-statistics (based on Ibragimov and Muller (2010) <DOI:10.1198/jbes.2009.08046>, pairs cluster bootstrapped t-statistics, and wild cluster bootstrapped t-statistics (the latter two techniques based on Cameron, Gelbach, and Miller (2008) <DOI:10.1162/rest.90.3.414>. Procedures are included for use with GLM, ivreg, plm (pooling or fixed effects), and mlogit models.

Calculates centrality indices additional to the igraph package centrality functions.

This package provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). CREAM uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. CREAM considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

Interface with and extract data from the United Nations Comtrade API <https://comtradeplus.un.org/>. Comtrade provides country level shipping data for a variety of commodities, these functions allow for easy API query and data returned as a tidy data frame.