Following Sommer (2022) <https://mediatum.ub.tum.de/1658240> portfolio level risk estimates (e.g. Value at Risk, Expected Shortfall) are estimated by modeling each asset univariately by an ARMA-GARCH model and then their cross dependence via a Vine Copula model in a rolling window fashion. One can even condition on variables/time series at certain quantile levels to stress test the risk measure estimates.

This package provides functions for tabulating and summarising categorical variables. Most functions are designed to work with dataframes, and use the tidyverse idiom of taking the dataframe as the first argument so they work within pipelines. Equivalent functions that operate directly on vectors are also provided where it makes sense. This package aims to make exploratory data analysis involving categorical variables quicker, simpler and more robust.

This package implements methods to fit Virtual Twins models (Foster et al. (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential effects in the context of clinical trials while controlling the probability of falsely detecting a differential effect when the conditional average treatment effect is uniform across the study population using parameter selection methods proposed in Wolf et al. (2022) <doi:10.1177/17407745221095855>.

Fitting models for, and simulation of, trend locally stationary wavelet (TLSW) time series models, which take account of time-varying trend and dependence structure in a univariate time series. The TLSW model, and its estimation, is described in McGonigle, Killick and Nunes (2022a) <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will likely want to start with the TLSW function.

Dalli is a high performance pure Ruby client for accessing memcached servers. Dalli supports:

• Simple and complex memcached configurations

• Fail-over between memcached instances

• Fine-grained control of data serialization and compression

• Thread-safe operation

• SSL/TLS connections to memcached

• SASL authentication.

The name is a variant of Salvador Dali for his famous painting The Persistence of Memory.

Various functions to fit models for non-normal repeated measurements, such as Binary Random Effects Models with Two Levels of Nesting, Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models, Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, Discrete-time Hidden Markov Chain Models, Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain, generalized nonlinear autoregression models, multivariate Gaussian copula models, generalized non-linear mixed models with one random effect, generalized non-linear mixed models using h-likelihood for one random effect, Repeated Measurements Models for Counts with Frailty or Serial Dependence, Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence, Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square contingency tables, correlated negative binomial models with Kalman update. References include Lindsey's text books, JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.

Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <doi:10.48550/arXiv.1611.06649>.

Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

This package provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weià , C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.

Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.

This creates code names that a user can consider for their organizations, their projects, themselves, people in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed) for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the user may be desiring as a code name or nickname.

This package provides a suite of routines for Clifford algebras, using the Map class of the Standard Template Library. Canonical reference: Hestenes (1987, ISBN 90-277-1673-0, "Clifford algebra to geometric calculus"). Special cases including Lorentz transforms, quaternion multiplication, and Grassmann algebra, are discussed. Vignettes presenting conformal geometric algebra, quaternions and split quaternions, dual numbers, and Lorentz transforms are included. The package follows disordR discipline.

This package provides a series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain.

Motifs within biological sequences show a significant role. This package utilizes a user-defined threshold value (window size and similarity) to create consensus segments or motifs through local alignment of dynamic programming with gap and it calculates the frequency of each identified motif, offering a detailed view of their prevalence within the dataset. It allows for thorough exploration and understanding of sequence patterns and their biological importance.

This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.

Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>. The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.

It is important to ensure that sensitive data is protected. This straightforward package is aimed at the end-user. Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns. A public key can be shared to allow others to encrypt data to be sent to you. This is particularly aimed a healthcare settings so patient data can be pseudonymised.

This package provides tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

This package provides a system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. It includes capabilities not supported by SQL', such as matching strings by stringr style regular expressions, and can compute comorbidity scores (Quan et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>) directly on a database server. The implementation is based on dbplyr with full tidyverse compatibility.

Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.

Framework for building modular Monte Carlo risk analysis models. It extends the capabilities of mc2d to facilitate working with multiple risk pathways, variates and scenarios. It provides tools to organize risk analysis in independent flexible modules, perform multivariate Monte Carlo node operations, automate the creation of Monte Carlo nodes and visualize risk analysis models. For more details see Ciria (2025) <https://nataliaciria.github.io/mcmodule/articles/mcmodule>.

Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The betaturn class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of taxaturn class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.