This is an substitute for the %V and %u formats which are not implemented on Windows. In addition, the package offers functions to convert from standard calender format yyyy-mm-dd to and from ISO 8601 week format yyyy-Www-d.

Nonparametric estimation on survival analysis under order-restrictions.

The IDSL.FSA package was designed to annotate standard .msp (mass spectra format) and .mgf (Mascot generic format) files using mass spectral entropy similarity, dot product (cosine) similarity, and normalized Euclidean mass error (NEME) followed by intelligent pre-filtering steps for rapid spectra searches. IDSL.FSA also provides a number of modules to convert and manipulate .msp and .mgf files. The IDSL.FSA workflow was integrated in the IDSL.CSA and IDSL.NPA packages introduced in <doi:10.1021/acs.analchem.3c00376>.

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.

Characterisation and calibration of single or multiple Ion Selective Electrodes (ISEs); activity estimation of experimental samples. Implements methods described in: Dillingham, P.W., Radu, T., Diamond, D., Radu, A. and McGraw, C.M. (2012) <doi:10.1002/elan.201100510>, Dillingham, P.W., Alsaedi, B.S.O. and McGraw, C.M. (2017) <doi:10.1109/ICSENS.2017.8233898>, Dillingham, P.W., Alsaedi, B.S.O., Radu, A., and McGraw, C.M. (2019) <doi:10.3390/s19204544>, and Dillingham, P.W., Alsaedi, B.S.O., Granados-Focil, S., Radu, A., and McGraw, C.M. (2020) <doi:10.1021/acssensors.9b02133>.

Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.

Get open statistical data and metadata disseminated by the National Statistics Institute of Spain (INE). The functions return data frames with the requested information thanks to calls to the INE API <https://www.ine.es/dyngs/DAB/index.htm?cid=1100>.

Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.

This package provides a comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like <https://www.gbif.org>, <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>, <https://shark.smhi.se/>, and <https://www.ecotaxa.org>. The package integrates with the MATLAB ifcb-analysis tool, which is described in Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides features for working with raw, manually classified, and machine learningâ classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research.

This package provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a R6 class. These containers are not faster than lists but their usage can be more convenient for certain situations.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

This package provides a set of functions for performing null hypothesis testing on samples of persistence diagrams using the theory of permutations. Currently, only two-sample testing is implemented. Inputs can be either samples of persistence diagrams themselves or vectorizations. In the former case, they are embedded in a metric space using either the Bottleneck or Wasserstein distance. In the former case, persistence data becomes functional data and inference is performed using tools available in the fdatest package. Main reference for the interval-wise testing method: Pini A., Vantini S. (2017) "Interval-wise testing for functional data" <doi:10.1080/10485252.2017.1306627>. Main reference for inference on populations of networks: Lovato, I., Pini, A., Stamm, A., & Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>.

Convenient functions to create ggplot2 graphics following the editorial guidelines of the Institute for Applied Economic Research (Ipea).

Some basic functions to implement belief functions including: transformation between belief functions using the method introduced by Philippe Smets <arXiv:1304.1122>, evidence combination, evidence discounting, decision-making, and constructing masses. Currently, thirteen combination rules and six decision rules are supported. It can also be used to generate different types of random masses when working on belief combination and conflict management.

The ISA is a biclustering algorithm that finds modules in an input matrix. A module or bicluster is a block of the reordered input matrix.

Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.

This package provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).

Collect marketing data from Instagram Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package performs iterative proportional updating given a seed table and an arbitrary number of marginal distributions. This is commonly used in population synthesis, survey raking, matrix rebalancing, and other applications. For example, a household survey may be weighted to match the known distribution of households by size from the census. An origin/ destination trip matrix might be balanced to match traffic counts. The approach used by this package is based on a paper from Arizona State University (Ye, Xin, et. al. (2009) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.537.723&rep=rep1&type=pdf>). Some enhancements have been made to their work including primary and secondary target balance/importance, general marginal agreement, and weight restriction.

Suite of functions to study animal incubation. At the core of incR lies an algorithm that allows for the scoring of incubation behaviour. Additionally, several functions extract biologically relevant metrics of incubation such as off-bout number and off-bout duration - for a review of avian incubation studies, see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.

This package contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" <https://www.routledge.com/Survival-Analysis-with-Interval-Censored-Data-A-Practical-Approach-with/Bogaerts-Komarek-Lesaffre/p/book/9781420077476>. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.

This package provides a set of tools for writing documents according to Geneva Graduate Institute conventions and regulations. The most common use is for writing and compiling theses or thesis chapters, as drafts or for examination with correct preamble formatting. However, the package also offers users to create HTML presentation slides with xaringan', complete problem sets, format posters, and, for course instructors, prepare a syllabus. The package includes additional functions for institutional color palettes, an institutional ggplot theme, a function for counting manuscript words, and a bibliographical analysis toolkit.

It provides in-place operators for R that are equivalent to +=', -=', *=', /= in C++. Those can be applied on integer|double vectors|matrices. You have also access to sweep operations (in-place).

Select set of parametric and non-parametric statistical tests. inferr builds upon the solid set of statistical tests provided in stats package by including additional data types as inputs, expanding and restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.