It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Conditional graphical lasso estimator is an extension of the graphical lasso proposed to estimate the conditional dependence structure of a set of p response variables given q predictors. This package provides suitable extensions developed to study datasets with censored and/or missing values. Standard conditional graphical lasso is available as a special case. Furthermore, the package provides an integrated set of core routines for visualization, analysis, and simulation of datasets with censored and/or missing values drawn from a Gaussian graphical model. Details about the implemented models can be found in Augugliaro et al. (2023) <doi: 10.18637/jss.v105.i01>, Augugliaro et al. (2020b) <doi: 10.1007/s11222-020-09945-7>, Augugliaro et al. (2020a) <doi: 10.1093/biostatistics/kxy043>, Yin et al. (2001) <doi: 10.1214/11-AOAS494> and Stadler et al. (2012) <doi: 10.1007/s11222-010-9219-7>.

Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.

Draws systematic samples from a population that follows linear trend. The function returns a matrix comprising of the required samples as its column vectors. The samples produced are highly efficient and the inter sampling variance is minimum. The scheme will be useful in various field like Bioinformatics where the samples are expensive and must be precise in reflecting the population by possessing least sampling variance.

Easy access to data from Brazil's population censuses. The package provides a simple and efficient way to download and read the data sets and the documentation of all the population censuses taken in and after 1960 in the country. The package is built on top of the Arrow platform <https://arrow.apache.org/docs/r/>, which allows users to work with larger-than-memory census data using dplyr familiar functions. <https://arrow.apache.org/docs/r/articles/arrow.html#analyzing-arrow-data-with-dplyr>.

This package provides a set of tools that can be used across data.frame and imputationList objects.

This package provides functions to access data from public RESTful APIs including FINDIC API', REST Countries API', World Bank API', and Nager.Date', retrieving real-time or historical data related to Chile such as financial indicators, holidays, international demographic and geopolitical indicators, and more. Additionally, the package includes curated datasets related to Chile, covering topics such as human rights violations during the Pinochet regime, electoral data, census samples, health surveys, seismic events, territorial codes, and environmental measurements. The package supports research and analysis focused on Chile by integrating open APIs with high-quality datasets from multiple domains. For more information on the APIs, see: FINDIC <https://findic.cl/>, REST Countries <https://restcountries.com/>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and Nager.Date <https://date.nager.at/Api>.

This package implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

Utilize the shiny interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the shiny session. Model diagnostics can be included in a rmarkdown document and rendered to desired output format.

Automatically builds 12 classification models from data. The package also returns 25 plots, 5 tables and a summary report.

This package provides a suite of machine learning algorithms written in C++ with the R interface contains several learning techniques for classification and regression. Predictive models include e.g., classification and regression trees with optional constructive induction and models in the leaves, random forests, kNN, naive Bayes, and locally weighted regression. All predictions obtained with these models can be explained and visualized with the ExplainPrediction package. This package is especially strong in feature evaluation where it contains several variants of Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, and DKM. These methods can be used for feature selection or discretization of numeric attributes. The OrdEval algorithm and its visualization is used for evaluation of data sets with ordinal features and class, enabling analysis according to the Kano model of customer satisfaction. Several algorithms support parallel multithreaded execution via OpenMP. The top-level documentation is reachable through ?CORElearn.

Conditional mixture model fitted via EM (Expectation Maximization) algorithm for model-based clustering, including parsimonious procedure, optimal conditional order exploration, and visualization.

Tests convergence in macro-financial panels combining Dynamic Factor Models (DFM) and mean-reverting, discrete-time Ornstein-Uhlenbeck/AR(1) factor processes. Provides: (i) static factor extraction with VAR stability checks, Portmanteau tests and rolling out-of-sample R^2, in the spirit of Stock and Watson (2002) <doi:10.1198/073500102317351921> and the Generalized Dynamic Factor Model of Forni, Hallin, Lippi and Reichlin (2000) <doi:10.1162/003465300559037>; (ii) cointegration analysis a la Johansen (1988) <doi:10.1016/0165-1889(88)90041-3>; (iii) Bayesian factor-OU/AR(1) estimation with convergence and half-life summaries grounded in Uhlenbeck and Ornstein (1930) <doi:10.1103/PhysRev.36.823> and Vasicek (1977) <doi:10.1016/0304-405X(77)90016-2>, with full Markov chain Monte Carlo convergence diagnostics; (iv) heteroskedasticity-consistent (HC) and, when the suggested sandwich (Zeileis (2004) <doi:10.18637/jss.v011.i10>) and lmtest packages are available, heteroskedasticity- and autocorrelation- consistent (HAC) robust inference, with a self-contained HC fallback; (v) coupling significance tests based on time-shift / block-bootstrap nulls that preserve marginal dynamics while breaking cross-series dependence; and (vi) optional PLS-based factor preselection (Mevik and Wehrens (2007) <doi:10.18637/jss.v018.i02>). Functions emphasize reproducibility (explicit seeds throughout) and clear, publication-ready summaries.

Predicts categorical or continuous outcomes while concentrating on a number of key points. These are Cross-validation, Accuracy, Regression and Rule of Ten or "one in ten rule" (CARRoT), and, in addition to it R-squared statistics, prior knowledge on the dataset etc. It performs the cross-validation specified number of times by partitioning the input into training and test set and fitting linear/multinomial/binary regression models to the training set. All regression models satisfying chosen constraints are fitted and the ones with the best predictive power are given as an output. Best predictive power is understood as highest accuracy in case of binary/multinomial outcomes, smallest absolute and relative errors in case of continuous outcomes. For binary case there is also an option of finding a regression model which gives the highest AUROC (Area Under Receiver Operating Curve) value. The option of parallel toolbox is also available. Methods are described in Peduzzi et al. (1996) <doi:10.1016/S0895-4356(96)00236-3> , Rhemtulla et al. (2012) <doi:10.1037/a0029315>, Riley et al. (2018) <doi:10.1002/sim.7993>, Riley et al. (2019) <doi:10.1002/sim.7992>.

Allows the user to categorise a continuous predictor variable in a logistic or a Cox proportional hazards regression setting, by maximising the discriminative ability of the model. I Barrio, I Arostegui, MX Rodriguez-Alvarez, JM Quintana (2015) <doi:10.1177/0962280215601873>. I Barrio, MX Rodriguez-Alvarez, L Meira-Machado, C Esteban, I Arostegui (2017) <https://www.idescat.cat/sort/sort411/41.1.3.barrio-etal.pdf>.

Significance test of Spearman's Rho or Kendall's Tau between short-range dependent random variables.

This package provides a device closing function which is able to crop graphics (e.g., PDF, PNG files) on Unix-like operating systems with the required underlying command-line tools installed.

ClickHouse (<https://clickhouse.com/>) is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. This DBI backend relies on the ClickHouse HTTP interface and support HTTPS protocol.

This package provides text analysis in R, focusing on the use of a tokenized text format. In this format, the positions of tokens are maintained, and each token can be annotated (e.g., part-of-speech tags, dependency relations). Prominent features include advanced Lucene-like querying for specific tokens or contexts (e.g., documents, sentences), similarity statistics for words and documents, exporting to DTM for compatibility with many text analysis packages, and the possibility to reconstruct original text from tokens to facilitate interpretation.

An engine for stochastic cellular automata. It provides a high-level interface to declare a model, which can then be simulated by various backends (Genin et al. (2023) <doi:10.1101/2023.11.08.566206>).

Imports PxStat data in JSON-stat format and (optionally) reshapes it into wide format. The Central Statistics Office (CSO) is the national statistical institute of Ireland and PxStat is the CSOs online database of Official Statistics. This database contains current and historical data series compiled from CSO statistical releases and is accessed at <https://data.cso.ie>. The CSO PxStat Application Programming Interface (API), which is accessed in this package, provides access to PxStat data in JSON-stat format at <https://data.cso.ie>. This dissemination tool allows developers machine to machine access to CSO PxStat data.

Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models.

This package produces statistical indicators of the impact of migration on the socio-demographic composition of an area. Three measures can be used: ratios, percentages and the Duncan index of dissimilarity. The input data files are assumed to be in an origin-destination matrix format, with each cell representing a flow count between an origin and a destination area. Columns are expected to represent origins, and rows are expected to represent destinations. The first row and column are assumed to contain labels for each area. See Rodriguez-Vignoli and Rowe (2018) <doi:10.1080/00324728.2017.1416155> for technical details.

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsafops R-package provides a collection of R-operators for the analysis and manipulation of CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).