This package provides functions to access data from the BrasilAPI and the REST Countries API', related to Brazil's postal codes, banks, holidays, company registrations, and international country indicators. Additionally, the package includes curated datasets related to Brazil, covering topics such as demographic data (males and females by state and year), river levels, environmental emission factors, film festivals, and yellow fever outbreak records. The package supports research and analysis focused on Brazil by integrating open APIs with high-quality datasets from multiple domains. For more details on the BrasilAPI', see <https://brasilapi.com.br/>, and for REST Countries', see <https://restcountries.com/>.

Fit calibrations curves for clinical prediction models and calculate several associated metrics (Eavg, E50, E90, Emax). Ideally predicted probabilities from a prediction model should align with observed probabilities. Calibration curves relate predicted probabilities (or a transformation thereof) to observed outcomes via a flexible non-linear smoothing function. pmcalibration allows users to choose between several smoothers (regression splines, generalized additive models/GAMs, lowess, loess). Both binary and time-to-event outcomes are supported. See Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>; Austin and Steyerberg (2019) <doi:10.1002/sim.8281>; Austin et al. (2020) <doi:10.1002/sim.8570>.

Computes bounds and sensitivity parameters as part of sensitivity analysis for selection bias. Different bounds are provided: the SV (Smith and VanderWeele), AF (assumption-free) bound, GAF (generalized AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity parameters for the SV and GAF bounds assume an additional dependence structure in form of a generalized M-structure. The bounds can be calculated for any structure as long as the necessary assumptions hold. See Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>, Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108> and Zetterstrom (2024) <doi:10.1515/em-2023-0033>.

Allows to detect spatial clusters of abnormal values on multivariate or functional data (Frévent et al. (2022) <doi:10.32614/RJ-2022-045>). See also: Frévent et al. (2023) <doi:10.1093/jrsssc/qlad017>, Smida et al. (2022) <doi:10.1016/j.csda.2021.107378>, Frévent et al. (2021) <doi:10.1016/j.spasta.2021.100550>. Cucala et al. (2019) <doi:10.1016/j.spasta.2018.10.002>, Cucala et al. (2017) <doi:10.1016/j.spasta.2017.06.001>, Jung and Cho (2015) <doi:10.1186/s12942-015-0024-6>, Kulldorff et al. (2009) <doi:10.1186/1476-072X-8-58>.

Offering enhanced statistical power compared to traditional hypothesis testing methods, informative hypothesis testing allows researchers to explicitly model their expectations regarding the relationships among parameters. An important software tool for this framework is restriktor'. The mmirestriktor package provides shiny web applications to implement some of the basic functionality of restriktor'. The mmirestriktor() function launches a shiny application for fitting and analyzing models with constraints. The FbarCards() function launches a card game application which can help build intuition about informative hypothesis testing. The iht_interpreter() helps interpret informative hypothesis testing results based on guidelines in Vanbrabant and Rosseel (2020) <doi:10.4324/9780429273872-14>.

Distance multivariance is a measure of dependence which can be used to detect and quantify dependence of arbitrarily many random vectors. The necessary functions are implemented in this packages and examples are given. It includes: distance multivariance, distance multicorrelation, dependence structure detection, tests of independence and copula versions of distance multivariance based on the Monte Carlo empirical transform. Detailed references are given in the package description, as starting point for the theoretic background we refer to: B. Böttcher, Dependence and Dependence Structures: Estimation and Visualization Using the Unifying Concept of Distance Multivariance. Open Statistics, Vol. 1, No. 1 (2020), <doi:10.1515/stat-2020-0001>.

This package contains tools for exploring Hardy-Weinberg equilibrium for diallelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium are included in the package, as well as functions for power computation and for the simulation of marker data under equilibrium and disequilibrium. Routines for dealing with markers on the X-chromosome are included. Functions for testing equilibrium in the presence of missing data by using multiple imputation are also provided. Implements several graphics for exploring the equilibrium status of a large set of diallelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.

Life expectancy is highly correlated over time among countries and between males and females. These associations can be used to improve forecasts. Here we have implemented a method for forecasting female life expectancy based on analysis of the gap between female life expectancy in a country compared with the record level of female life expectancy in the world. Second, to forecast male life expectancy, the gap between male life expectancy and female life expectancy in a country is analysed. We named this method the Double-Gap model. For a detailed description of the method see Pascariu et al. (2018). <doi:10.1016/j.insmatheco.2017.09.011>.

Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

Statistical analysis of archaeological dates and groups of dates. This package allows to post-process Markov Chain Monte Carlo (MCMC) simulations from ChronoModel <https://chronomodel.com/>, Oxcal <https://c14.arch.ox.ac.uk/oxcal.html> or BCal <https://bcal.shef.ac.uk/>. It provides functions for the study of rhythms of the long term from the posterior distribution of a series of dates (tempo and activity plot). It also allows the estimation and visualization of time ranges from the posterior distribution of groups of dates (e.g. duration, transition and hiatus between successive phases) as described in Philippe and Vibet (2020) <doi:10.18637/jss.v093.c01>.

Create data summaries for quality control, extensive reports for exploring data, as well as publication-ready univariate or bivariate tables in several formats (plain text, HTML,LaTeX, PDF, Word or Excel. Create figures to quickly visualise the distribution of your data (boxplots, barplots, normality-plots, etc.). Display statistics (mean, median, frequencies, incidences, etc.). Perform the appropriate tests (t-test, Analysis of variance, Kruskal-Wallis, Fisher, log-rank, ...) depending on the nature of the described variable (normal, non-normal or qualitative). Summarize genetic data (Single Nucleotide Polymorphisms) data displaying Allele Frequencies and performing Hardy-Weinberg Equilibrium tests among other typical statistics and tests for these kind of data.

The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) <doi:10.2307/2282833>, Abrevaya (1999) <doi:10.1016/S0165-1765(98)00255-9>, Christensen (2005) <doi:10.1007/BF02736122> and Emara (2024) <https://learningcpp.org/>. This implementation is described in Vargas Sepulveda (2024) <doi:10.48550/arXiv.2408.09618>.

Stock, Options and Futures Trading Strategies for Traders and Investors with Bearish Outlook. The indicators, strategies, calculations, functions and all other discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Guy Cohen (â The Bible of Options Strategies (2nd ed.)â , 2015, ISBN: 9780133964028). Juan A. Serur, Juan A. Serur (â 151 Trading Strategiesâ , 2018, ISBN: 9783030027919). Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 385-453)", 2019, ISBN: 9781119593577). John C. Hull (â Options, Futures, and Other Derivatives (11th ed.)â , 2022, ISBN: 9780136939979).

This package provides a function called COTUCKER3() (Co-Inertia Analysis + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of matrices, as well as other functions called PCA() (Principal Component Analysis) and BGA() (Between-Groups Analysis), which perform analysis of one matrix, COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA() (Partial Triadic Analysis), STATIS(), STATISDUAL() and TUCKER3(), which perform analysis of a sequence of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO() (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS method), which also perform analysis of two sequences of matrices.

Identification of equilibrium locations in location games (Hotelling (1929) <doi:10.2307/2224214>). In these games, two competing actors place customer-serving units in two locations simultaneously. Customers make the decision to visit the location that is closest to them. The functions in this package include Prim algorithm (Prim (1957) <doi:10.1002/j.1538-7305.1957.tb01515.x>) to find the minimum spanning tree connecting all network vertices, an implementation of Dijkstra algorithm (Dijkstra (1959) <doi:10.1007/BF01386390>) to find the shortest distance and path between any two vertices, a self-developed algorithm using elimination of purely dominated strategies to find the equilibrium, and several plotting functions.

This package provides a non-parametric test founded upon the principles of the Kolmogorov-Smirnov (KS) test, referred to as the KS Predictive Accuracy (KSPA) test. The KSPA test is able to serve two distinct purposes. Initially, the test seeks to determine whether there exists a statistically significant difference between the distribution of forecast errors, and secondly it exploits the principles of stochastic dominance to determine whether the forecasts with the lower error also reports a stochastically smaller error than forecasts from a competing model, and thereby enables distinguishing between the predictive accuracy of forecasts. KSPA test has been described in : Hassani and Silva (2015) <doi:10.3390/econometrics3030590>.

This package provides functions to access data from the World Bank API and the REST Countries API', related to Mexico's economic indicators, population statistics, literacy rates, and international geopolitical information. Additionally, the package includes curated datasets related to Mexico, including air quality monitoring stations, pollution zones, income surveys, postal abbreviations, election studies, forest productivity, and demographic data by state. The package supports research and analysis focused on Mexico by integrating reliable global APIs with structured national datasets drawn from open and academic sources. For more details on the World Bank API', see <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and for the REST Countries API', see <https://restcountries.com/>.

There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period.

Fit Bayesian time series models using Stan for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

An interactive platform for clustering analysis and teaching based on the shiny web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) <doi:10.1007/s11222-007-9033-z> for spectral clustering.

This package provides a workflow based on scTenifoldNet to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the geneâ s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the geneâ s function in the analyzed cells.

Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.

These functions were developed to support statistical analysis on functional covariance operators. The package contains functions to: - compute 2-Wasserstein distances between Gaussian Processes as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - compute the Wasserstein barycenter (Frechet mean) as in Masarotto, Panaretos & Zemel (2019) <doi:10.1007/s13171-018-0130-1>; - perform analysis of variance testing procedures for functional covariances and tangent space principal component analysis of covariance operators as in Masarotto, Panaretos & Zemel (2022) <arXiv:2212.04797>. - perform a soft-clustering based on the Wasserstein distance where functional data are classified based on their covariance structure as in Masarotto & Masarotto (2023) <doi:10.1111/sjos.12692>.

Reduction-based techniques for cost-sensitive multi-class classification, in which each observation has a different cost for classifying it into one class, and the goal is to predict the class with the minimum expected cost for each new observation. Implements Weighted All-Pairs (Beygelzimer, A., Langford, J., & Zadrozny, B., 2008, <doi:10.1007/978-0-387-79361-0_1>), Weighted One-Vs-Rest (Beygelzimer, A., Dani, V., Hayes, T., Langford, J., & Zadrozny, B., 2005, <https://dl.acm.org/citation.cfm?id=1102358>) and Regression One-Vs-Rest. Works with arbitrary classifiers taking observation weights, or with regressors. Also implements cost-proportionate rejection sampling for working with classifiers that don't accept observation weights.