An R implementation of the algorithms described in Reingold and Dershowitz (4th ed., Cambridge University Press, 2018) <doi:10.1017/9781107415058>, allowing conversion between many different calendar systems. Cultural and religious holidays from several calendars can be calculated.

Joint distribution of number of crossings and the longest run in a series of independent Bernoulli trials. The computations uses an iterative procedure where computations are based on results from shorter series. The procedure conditions on the start value and partitions by further conditioning on the position of the first crossing (or none).

This package provides a copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2019). "Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. <doi:10.1007/s00362-016-0822-3>.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025) "Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.

The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research.

Allows you to connect to data sources across the crypto ecosystem. This data can enable a range of activity such as portfolio tracking, programmatic trading, or industry analysis. The package is described in French (2024) <https://github.com/TrevorFrench/cryptotrackr/wiki>.

Amends errors, augments data and aids analysis of John Snow's map of the 1854 London cholera outbreak.

Assesses the quality of estimates made by complex sample designs, following the methodology developed by the National Institute of Statistics Chile (Household Survey Standard 2020, <https://www.ine.cl/docs/default-source/institucionalidad/buenas-pr%C3%A1cticas/clasificaciones-y-estandares/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-publicaci%C3%B3n-27022020.pdf>), (Economics Survey Standard 2024, <https://www.ine.gob.cl/docs/default-source/buenas-practicas/directrices-metodologicas/estandares/documentos/est%C3%A1ndar-evaluaci%C3%B3n-de-calidad-de-estimaciones-econ%C3%B3micas.pdf?sfvrsn=201fbeb9_2>) and by Economic Commission for Latin America and Caribbean (2020, <https://repositorio.cepal.org/bitstream/handle/11362/45681/1/S2000293_es.pdf>), (2024, <https://repositorio.cepal.org/server/api/core/bitstreams/f04569e6-4f38-42e7-a32b-e0b298e0ab9c/content>).

Set chunk hooks for R Markdown documents <https://rmarkdown.rstudio.com/>, and improve user experience. For example, change units of figure sizes, benchmark chunks, and number lines on code blocks.

Collection of indices and tools relating to clinical research that aid epidemiological cohort or retrospective chart review with big data. All indices and tools take commonly used lab values, patient demographics, and clinical measurements to compute various risk and predictive values for survival or further classification/stratification. References to original literature and validation contained in each function documentation. Includes all commonly available calculators available online.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Includes binning categorical variables into lesser number of categories based on t-test, converting categorical variables into continuous features using the mean of the response variable for the respective categories, understanding the relationship between the response variable and predictor variables using data transformations.

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

Provide step by step guided tours of Shiny applications.

This package implements functions for comparing strings, sequences and numeric vectors for clustering and record linkage applications. Supported comparison functions include: generalized edit distances for comparing sequences/strings, Monge-Elkan similarity for fuzzy comparison of token sets, and L-p distances for comparing numeric vectors. Where possible, comparison functions are implemented in C/C++ to ensure good performance.

Computing elliptical joint confidence regions at a specified confidence level. It provides the flexibility to estimate either classical or robust confidence regions, which can be visualized in 2D or 3D plots. The classical approach assumes normality and uses the mean and covariance matrix to define the confidence regions. Alternatively, the robustified version employs estimators like minimum covariance determinant (MCD) and M-estimator, making them less sensitive to outliers and departures from normality. Furthermore, the functions allow users to group the dataset based on categorical variables and estimate separate confidence regions for each group. This capability is particularly useful for exploring potential differences or similarities across subgroups within a dataset. Varmuza and Filzmoser (2009, ISBN:978-1-4200-5947-2). Johnson and Wichern (2007, ISBN:0-13-187715-1). Raymaekers and Rousseeuw (2019) <DOI:10.1080/00401706.2019.1677270>.

The Central Bank of the Republic of Turkey (CBRT) provides one of the most comprehensive time series databases on the Turkish economy. The CBRT package provides functions for accessing the CBRT's electronic data delivery system <https://evds2.tcmb.gov.tr/>. It contains the lists of all data categories and data groups for searching the available variables (data series). As of November 3, 2024, there were 40,826 variables in the dataset. The lists of data categories and data groups can be updated by the user at any time. A specific variable, a group of variables, or all variables in a data group can be downloaded at different frequencies using a variety of aggregation methods.

Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".

Under natural conditions, nest temperatures fluctuate daily around a mean value, whereas in captivity they are often held constant. The Constant Temperature Equivalent is designed to bridge the gap between the two by calculating a single temperature value for wild nests that corresponds with the amount of development that would occur in an incubator set to the same temperature. The theory and formulas behind this method were developed by Professor Author Georges and are implemented here as a single function.

This package provides easy and consistent time conversion for public health purposes. The time conversion functions provided here are between date, ISO week, ISO yearweek, ISO year, calendar month/year, season, season week.

This package provides a dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp() can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp() and backtracking.sc.dp() can be used. See Szkaliczki, T. (2016) "clustering.sc.dp: Optimal Clustering with Sequential Constraint by Using Dynamic Programming" <doi: 10.32614/RJ-2016-022> for more information.

In discrimination experiments candidates are sent on the same test (e.g. job, house rental) and one examines whether they receive the same outcome. The number of non negative answers are first examined in details looking for outcome differences. Then various statistics are computed. This package can also be used for analyzing the results from random experiments.

Variable selection for Gaussian model-based clustering as implemented in the mclust package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting mclust models. By default the algorithm uses a sequential search, but parallelisation is also available.