This package provides functions for determining and evaluating high-risk zones and simulating and thinning point process data, as described in Determining high risk zones using point process methodology - Realization by building an R package Seibold (2012) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> and Determining high-risk zones for unexploded World War II bombs by using point process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.

Can be used for paternity and maternity assignment and outperforms conventional methods where closely related individuals occur in the pool of possible parents. The method compares the genotypes of offspring with any combination of potentials parents and scores the number of mismatches of these individuals at bi-allelic genetic markers (e.g. Single Nucleotide Polymorphisms). It elaborates on a prior exclusion method based on the Homozygous Opposite Test (HOT; Huisman 2017 <doi:10.1111/1755-0998.12665>) by introducing the additional exclusion criterion HIPHOP (Homozygous Identical Parents, Heterozygous Offspring are Precluded; Cockburn et al., in revision). Potential parents are excluded if they have more mismatches than can be expected due to genotyping error and mutation, and thereby one can identify the true genetic parents and detect situations where one (or both) of the true parents is not sampled. Package hiphop can deal with (a) the case where there is contextual information about parentage of the mother (i.e. a female has been seen to be involved in reproductive tasks such as nest building), but paternity is unknown (e.g. due to promiscuity), (b) where both parents need to be assigned, because there is no contextual information on which female laid eggs and which male fertilized them (e.g. polygynandrous mating system where multiple females and males deposit young in a common nest, or organisms with external fertilisation that breed in aggregations). For details: Cockburn, A., Penalba, J.V.,Jaccoud, D.,Kilian, A., Brouwer, L., Double, M.C., Margraf, N., Osmond, H.L., van de Pol, M. and Kruuk, L.E.B. (in revision). HIPHOP: improved paternity assignment among close relatives using a simple exclusion method for bi-allelic markers. Molecular Ecology Resources, DOI to be added upon acceptance.

An implementation of the modelling and reporting features described in reference textbook and guidelines (Briggs, Andrew, et al. Decision Modelling for Health Economic Evaluation. Oxford Univ. Press, 2011; Siebert, U. et al. State-Transition Modeling. Medical Decision Making 32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and non-homogeneous Markov models), etc.

Programmatic interface to the Harmonized World Soil Database HWSD web services (<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247>). Allows for easy downloads of HWSD soil data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded data are provided.

Set of R functions to be coupled with the xeus-r jupyter kernel in order to drive execution of code in notebook input cells, how R objects are to be displayed in output cells, and handle two way communication with the front end through comms.

This package provides a tool to format R markdown with CSS ids for HTML output. The tool may be most helpful for those using markdown to create reproducible documents. The biggest limitations in formatting is the knowledge of CSS by the document authors.

Calculate Hopkins statistic to assess the clusterability of data. See Wright (2023) <doi:10.32614/RJ-2022-055>.

This package implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) <arXiv:2003.04314>. Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.

Manipulate data through memory-mapped files, as vectors, matrices or arrays. Basic arithmetic functions are implemented, but currently no matrix arithmetic. Can write and read descriptor files for compatibility with the bigmemory package.

Implementation of S4 class of sets and multisets of numbers. The implementation is based on the hash table from the package hash'. Quick operations are allowed when the set is a dynamic object. The implementation is discussed in detail in Ceoldo and Wit (2023) <arXiv:2304.09809>.

This package provides a generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.

When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. Reference: Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.

This package provides methods to test whether time series is consistent with white noise. Two new tests based on Haar wavelets and general wavelets described by Nason and Savchev (2014) <doi:10.1002/sta4.69> are provided and, for comparison purposes this package also implements the B test of Bartlett (1967) <doi:10.2307/2333850>. Functionality is provided to compute an approximation to the theoretical power of the general wavelet test in the case of general ARMA alternatives.

Facilitates hierarchical clustering analysis with functions to read data in txt', xlsx', and xls formats, apply normalization techniques to the dataset, perform hierarchical clustering and construct scatter plot from principal component analysis to evaluate the groups obtained.

Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms outputs to gating strategies outputs.

This package provides a procedure that fits derivative curves based on a sequence of quotient differences. In a hierarchical setting the package produces estimates of subject-specific and group-specific derivative curves. In a non-hierarchical setting the package produces a single derivative curve.

Allows for painless use of the Metopio health atlas APIs <https://metopio.com/health-atlas> to explore and import data. Metopio health atlases store open public health data. See what topics (or indicators) are available among specific populations, periods, and geographic layers. Download relevant data along with geographic boundaries or point datasets. Spatial datasets are returned as sf objects.

Cellular responses to perturbations are highly heterogeneous and depend largely on the initial state of cells. Connecting post-perturbation cells via cellular trajectories to untreated cells (e.g. by leveraging metabolic labeling information) enables exploitation of intercellular heterogeneity as a combined knock-down and overexpression screen to identify pathway modulators, termed Heterogeneity-seq (see Berg et al <doi:10.1101/2024.10.28.620481>). This package contains functions to generate cellular trajectories based on scSLAM-seq (single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing) time courses, functions to identify pathway modulators and to visualize the results.

Creating effective colour palettes for figures is challenging. This package generates and plot palettes of optimally distinct colours in perceptually uniform colour space, based on iwanthue <http://tools.medialab.sciences-po.fr/iwanthue/>. This is done through k-means clustering of CIE Lab colour space, according to user-selected constraints on hue, chroma, and lightness.

Interface to the HERE REST APIs <https://developer.here.com/develop/rest-apis>: (1) geocode and autosuggest addresses or reverse geocode POIs using the Geocoder API; (2) route directions, travel distance or time matrices and isolines using the Routing', Matrix Routing and Isoline Routing APIs; (3) request real-time traffic flow and incident information from the Traffic API; (4) find request public transport connections and nearby stations from the Public Transit API; (5) request intermodal routes using the Intermodal Routing API; (6) get weather forecasts, reports on current weather conditions, astronomical information and alerts at a specific location from the Destination Weather API. Locations, routes and isolines are returned as sf objects.

Calculates the interval estimates for the parameters of linear models with heteroscedastic regression using bootstrap - (Wild Bootstrap) and double bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using the percentile bootstrap and percentile bootstrap double. The package can calculate consistent estimates of the covariance matrix of the parameters of linear regression models with heteroscedasticity of unknown form. The package also provides a function to consistently calculate the covariance matrix of the parameters of linear models with heteroscedasticity of unknown form. The bootstrap methods exported by the package are based on the master's thesis of the first author, available at <https://raw.githubusercontent.com/prdm0/hcci/master/references/dissertacao_mestrado.pdf>. The hcci package in previous versions was cited in the book VINOD, Hrishikesh D. Hands-on Intermediate Econometrics Using R: Templates for Learning Quantitative Methods and R Software. 2022, p. 441, ISBN 978-981-125-617-2 (hardcover). The simple bootstrap schemes are based on the works of Cribari-Neto F and Lima M. G. (2009) <doi:10.1080/00949650801935327>, while the double bootstrap schemes for the parameters that index the linear models with heteroscedasticity of unknown form are based on the works of Beran (1987) <doi:10.2307/2336685>. The use of bootstrap for the calculation of interval estimates in regression models with heteroscedasticity of unknown form from a weighting of the residuals was proposed by Wu (1986) <doi:10.1214/aos/1176350142>. This bootstrap scheme is known as weighted or wild bootstrap.

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an age-structured spatio-temporal model for infectious disease counts. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2015, by age group, city district and week, using additional contact data from the POLYMOD survey. This package contains the data and code to reproduce the results from the paper, see demo("hhh4contacts")'.

Develops algorithms for fitting, prediction, simulation and initialization of the following models (1)- hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, (2)- nonparametric mixture of B-splines emissions (Langrock et al., 2015 <doi:10.1111/biom.12282>), (3)- regime switching regression model (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive hidden hybrid Markov/semi-Markov model, (4)- spline-based nonparametric estimation of additive state-switching models (Langrock et al., 2018 <doi:10.1111/stan.12133>) (5)- robust emission model proposed by Qin et al, 2024 <doi:10.1007/s10479-024-05989-4> (6)- several emission distributions, including mixture of multivariate normal (which can also handle missing data using EM algorithm) and multi-nomial emission (for modeling polymer or DNA sequences) (7)- tools for prediction of future state sequence, computing the score of a new sequence, splitting the samples and sequences to train and test sets, computing the information measures of the models, computing the residual useful lifetime (reliability) and many other useful tools ... (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its arxiv version: <doi:10.48550/arXiv.2109.12489>).