This package provides a time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

Implementation of different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. It serves to compute empirical cumulative distributions and also kernel density and hazard functions using different bandwidth selectors. Several real data sets are included.

This package provides functions and an example dataset for the psychometric theory of knowledge spaces. This package implements data analysis methods and procedures for simulating data and quasi orders and transforming different formulations in knowledge space theory. See package?DAKS for an overview.

Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman et al. 1993 Journal of Vegetation Science <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.

This package provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). A detailed introduction to the package is provided by Willwerscheid and Stephens (2021) <arXiv:2110.00152>.

This package provides a group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.

Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.

This package performs likelihood criterion analysis using the Laplace regression model to determine its optimal subset of variables. The methodology is based on Guo et al. (2023), LIC criterion for optimal subset selection in distributed interval estimation <doi:10.1080/02331888.2020.1823979>.

Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2020) <doi:10.1186/s12859-019-3324-1>.

This package provides functions to calculate hazard and survival function of Multi-Stage Clonal Expansion Models used in cancer epidemiology. For the Two-Stage Clonal Expansion Model an exact solution is implemented assuming piecewise constant parameters. Numerical solutions are provided for its extensions.

This package provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.

This package implements projected sparse Gaussian process Kriging (Ingram et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the intamap package. More details on implementation (Barillec et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).

The letters qe in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface.

This package provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.

This package provides a shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 477:112711. <doi:10.1016/j.jim.2019.112711>.

This package implements the estimation of local (and global) association measures: Lewontin's D, Ducher's Z, pointwise mutual information, normalized pointwise mutual information and chi-squared residuals. The significance of local (and global) association is accessed using p-values estimated by permutations.

This package provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data.

This package provides functions for reading array comparative genomic hybridization (aCGH) data from image analysis output files and clone information files, creation of aCGH objects for storing these data. Basic methods are accessing/replacing, subsetting, printing and plotting aCGH objects.

R-scape discovers RNA secondary structure consensus elements. These elements include riboswitches and ribozymes. It utilizes probabilistic modeling of sequence alignments, explicitly considering folding dependencies. The tool enables the de novo search for new structural elements and facilitates comparative analysis of known RNA families.

This package provides S3 classes and methods to create and work with year-quarter, year-month and year-isoweek vectors. Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types.

This package provides tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). The area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

This package generates well-known integer sequences. The gmp package is adopted for computing with arbitrarily large numbers. Every function has a hyperlink to its corresponding item in the On-Line Encyclopedia of Integer Sequences (OEIS) in the function help page.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019, <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether if an observed trait clustering pattern is related to an increasing environmental constrain. The method 1) determines whether exists or not a trait clustering on the sampled communities and 2) assess if the observed clustering signal is related or not to an increasing environmental constrain along an environmental gradient. Also, when the effect of the environmental gradient is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.