Dichotomous and polytomous data analysis and their scoring using the unidimensional Item Response Theory model (Chalmers (2012) <doi:10.18637/jss.v048.i06>) with user-friendly graphic User Interface. Suitable for beginners who are learning item response theory.

Compute onestep and multistep time series forecasts for machine learning models.

An implementation of the "FAST-9" corner detection algorithm explained in the paper FASTER and better: A machine learning approach to corner detection by Rosten E., Porter R. and Drummond T. (2008), available at <doi:10.48550/arXiv.0810.2434>. The package allows to detect corners in digital images.

This package provides functions to Interact with the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk/web/index.aspx>.

This package provides functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].

Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Allows access to data from the Rio de Janeiro Public Security Institute (ISP), such as criminal statistics, data on gun seizures and femicide. The package also contains the spatial data of Pacifying Police Units (UPPs) and Integrated Public Safety Regions, Areas and Circumscriptions.

Develops stochastic models based on the Theory of Island Biogeography (TIB) of MacArthur and Wilson (1967) <doi:10.1023/A:1016393430551> and extensions. It implements methods to estimate colonization and extinction rates (including environmental variables) given presence-absence data, simulates community assembly, and performs model selection.

This package provides efficient implementation of the Isolate-Detect methodology for the consistent estimation of the number and location of multiple change-points in one-dimensional data sequences from the "deterministic + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>. Currently implemented scenarios are: piecewise-constant signal with Gaussian noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.

Support for implicit expansion of arrays in operations involving arrays of mismatching sizes. This pattern is known as "broadcasting" in Python and "implicit expansion" in Matlab and is explained for example in the article "Array programming with NumPy" by C. R. Harris et al. (2020) <doi:10.1038/s41586-020-2649-2>.

Using shiny to demo igraph package makes learning graph theory easy and fun.

Semiparametric regression models on the cumulative incidence function for interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) /doi10.1002/sim.7350 and the models with missing event types as described in Park, Bakoyannis, Zhang, & Yiannoutsos (2021) \doi10.1093/biostatistics/kxaa052. The proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.

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.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.

Builds statistical control charts with exact limits for univariate and multivariate cases.

Get image statistics based on processing fluency theory. The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

This package provides a variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2020) <doi:10.1017/pan.2019.48> and Hangartner, Marbach, Henckel, Maathuis, Kelz and Keele (2021) <doi:10.48550/arXiv.2103.06328>.

Boxplots adapted to the happenstance of missing observations where drop-out probabilities can be given by the practitioner or modelled using auxiliary covariates. The paper of "Zhang, Z., Chen, Z., Troendle, J. F. and Zhang, J.(2012) <doi:10.1111/j.1541-0420.2011.01712.x>", proposes estimators of marginal quantiles based on the Inverse Probability Weighting method.

Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.

Some tools to assist with converting International Organization for Standardization (ISO) standard 11784 (ISO11784) animal ID codes between 4 recognised formats commonly displayed on Passive Integrated Transponder (PIT) tag readers. The most common formats are 15 digit decimal, e.g., 999123456789012, and 13 character hexadecimal dot format, e.g., 3E7.1CBE991A14. These are referred to in this package as isodecimal and isodothex. The other two formats are the raw hexadecimal representation of the ISO11784 binary structure (see <https://en.wikipedia.org/wiki/ISO_11784_and_ISO_11785>). There are two flavours of this format, a left and a right variation. Which flavour a reader happens to output depends on if the developers decided to reverse the binary number or not before converting to hexadecimal, a decision based on the fact that the PIT tags will transmit their binary code Least Significant Bit (LSB) first, or backwards basically.

Calculates irrigation water quality ratios and has functions that could be used to plot several popular diagrams for irrigation water quality classification.