Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.

Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour from empirical time series data. One of the main features of chaos is the well known initial value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial value sensitive property estimating the Lyapunov exponents. The DChaos package provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos based on the Lyapunov exponent in order to know if the data generating process behind time series behave chaotically or not.

Monthly download stats of CRAN and Bioconductor packages. Download stats of CRAN packages is from the RStudio CRAN mirror', see <https://cranlogs.r-pkg.org:443>. Bioconductor package download stats is at <https://bioconductor.org/packages/stats/>.

Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records.

Use dynamic programming method to solve l1 convex clustering with identical weights.

Calculate adjusted means and proportions of a variable by groups defined by another variable by direct standardisation, standardised to the structure of the dataset.

This package performs various analyzes of descriptive statistics, including correlations, graphics and tables.

Interactive R tutorials and datasets for the textbook Field (2026), "Discovering Statistics Using R and RStudio", <https://www.discovr.rocks/>. Interactive tutorials cover general workflow in R and RStudio', summarizing data, visualizing data, fitting models and bias, correlation, the general linear model (GLM), moderation, mediation, missing values, comparing means using the GLM (analysis of variance), comparing adjusted means (analysis of covariance), factorial designs, multilevel models, repeated measures designs, growth models, exploratory factor analysis (EFA), loglinear analysis, and logistic regression. There are no functions, only datasets and interactive tutorials.

Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function comparison, covariate matching, and energy decomposition. Relevant works for the developed functions are: funGP() - Prakash et al. (2022) <doi:10.1080/00401706.2021.1905073>, AMK() - Lee et al. (2015) <doi:10.1080/01621459.2014.977385>, tempGP() - Prakash et al. (2022) <doi:10.1080/00401706.2022.2069158>, ComparePCurve() - Ding et al. (2021) <doi:10.1016/j.renene.2021.02.136>, deltaEnergy() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, syncSize() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, imptPower() - Latiffianti et al. (2022) <doi:10.1002/we.2722>, All other functions - Ding (2019, ISBN:9780429956508).

This package provides a system for analyzing descriptive representation, especially for comparing the composition of a political body to the population it represents. Users can compute the expected degree of representation for a body under a random sampling model, the expected degree of representation variability, as well as representation scores from observed political bodies. The package is based on Gerring, Jerzak, and Oncel (2024) <doi:10.1017/S0003055423000680>.

Alluvial plots are similar to sankey diagrams and visualise categorical data over multiple dimensions as flows. (Rosvall M, Bergstrom CT (2010) Mapping Change in Large Networks. PLoS ONE 5(1): e8694. <doi:10.1371/journal.pone.0008694> Their graphical grammar however is a bit more complex then that of a regular x/y plots. The ggalluvial package made a great job of translating that grammar into ggplot2 syntax and gives you many options to tweak the appearance of an alluvial plot, however there still remains a multi-layered complexity that makes it difficult to use ggalluvial for explorative data analysis. easyalluvial provides a simple interface to this package that allows you to produce a decent alluvial plot from any dataframe in either long or wide format from a single line of code while also handling continuous data. It is meant to allow a quick visualisation of entire dataframes with a focus on different colouring options that can make alluvial plots a great tool for data exploration.

Presents two methods to estimate the parameters mu', sigma', and tau of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The QMLE method allows a choice between three different estimation algorithms for these parameters : neldermead ('NEMD'), fminsearch ('FMIN'), and nlminb ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.

This package provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.

Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).

Bayesian quantile-regression routines for dynamic state-space models and static regression under the extended asymmetric Laplace (exAL) error distribution. The dynamic state-space models are extended dynamic quantile linear models (exDQLMs). The package combines dynamic exDQLM inference via LDVB, MCMC, and legacy ISVB with static exAL regression via LDVB and MCMC, reduced AL/DQLM paths through fixed skewness, component builders for trend/seasonality/regression blocks, static shrinkage priors including ridge, regularized horseshoe, and rhs_ns', evidence lower bound diagnostics, optional C++ accelerators, and posterior predictive synthesis across separately fitted quantiles through quantileSynthesis()'. Dynamic exDQLM methods are described in Barata et al. (2020) <doi:10.1214/21-AOAS1497>.

Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

Experiences studies are an integral component of the actuarial control cycle. Regardless of the decrement or policyholder behavior of interest, the analyses conducted is often the same. Ultimately, this package aims to reduce time spent writing the same code used for different experience studies, therefore increasing the time for to uncover new insights inherit within the relevant experience.

The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to decluster earthquake data catalogs. Usually, the calculation of standard errors of the ETAS model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an ETAS model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal ETAS model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature.

Various recursive two-stage models to address the endogeneity issue of treatment variables in observational study or mediators in experiments. The details of the models are discussed in Peng (2023) <doi:10.1287/isre.2022.1113>.

If translate English or Chinese sentence, there is a faster way for R user. You can pass in an English or Chinese sentence, ecce package support both English and Chinese translation. It also support browse translation results in website. In addition, also support obtain the pinyin of the Chinese character, you can more easily understand the pronunciation of the Chinese character.

This package performs the exact test on whether there is a difference between two survival curves. Exact confidence interval for the hazard ratio can also be generated for the Cox model.

This package provides tools for road-constrained, least-cost pedestrian evacuation modeling. The package provides reusable functions for preparing hazard zones, generating road-based evacuation origin points, identifying escape/safety points, creating slope-based conductance surfaces, calculating least-cost distance to safety, and converting distance outputs into evacuation-time polygons. It is designed to support workflows like tsunami evacuation modeling while remaining adaptable to other regions and hazards. Tsunami-specific helpers support separate land-only hazard zones, water-combined escape zones, road-aware escape boundaries, and study-area inset cropping for quality assurance and quality control. Methods build on Cordero et al. (2025) <doi:10.1007/s44367-025-00018-y>, Lewis (2021) <doi:10.1007/s10816-021-09522-w>, and Joseph Lewis's leastcostpath package (2023) <https://CRAN.R-project.org/package=leastcostpath>.

This package provides a predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system.

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.