This package implements the instruments for complex-valued modelling, including time series analysis and forecasting. This is based on the monograph by Svetunkov & Svetunkov (2024) <doi: 10.1007/978-3-031-62608-1>.

Unified access to carbon market data from compliance emissions trading systems ('EU ETS', UK ETS', RGGI', California Cap-and-Trade) and voluntary carbon markets (Verra, Gold Standard, American Carbon Registry, Climate Action Reserve, via the Berkeley Voluntary Registry Offsets Database and the CarbonPlan OffsetsDB API). Includes cross-market price data from the International Carbon Action Partnership ('ICAP') Allowance Price Explorer <https://icapcarbonaction.com/en/ets-prices>, global carbon pricing from the World Bank Carbon Pricing Dashboard <https://carbonpricingdashboard.worldbank.org/>, and the historical RFF World Carbon Pricing Database following Dolphin, Pollitt and Newbery (2020) <doi:10.1038/s41597-022-01659-x>. Data is downloaded from public sources on first use and cached locally.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

Complex hypothesis testing through conditional cumulative distribution function estimation. Method is detailed in: Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2021). "Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis", bioRxiv <doi:10.1101/2021.05.21.445165>.

Implementation of case-control data analysis using likelihood ratio approaches and logistic regression for the classification of variants of uncertain significance (VUS) in breast, ovarian, or custom cancer susceptibility genes.

Conditional distance correlation <doi:10.1080/01621459.2014.993081> is a novel conditional dependence measurement of two multivariate random variables given a confounding variable. This package provides conditional distance correlation, performs the conditional distance correlation sure independence screening procedure for ultrahigh dimensional data <https://www3.stat.sinica.edu.tw/statistica/J28N1/J28N114/J28N114.html>, and conducts conditional distance covariance test for conditional independence assumption of two multivariate variable.

This package implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

Use machine learning algorithms and advanced geographic information system tools to build Species Distribution Modeling in a extensible and modern fashion.

This package provides a set of functions for counterfactual decomposition (cfdecomp). The functions available in this package decompose differences in an outcome attributable to a mediating variable (or sets of mediating variables) between groups based on counterfactual (causal inference) theory. By using Monte Carlo (MC) integration (simulations based on empirical estimates from multivariable models) we provide added flexibility compared to existing (analytical) approaches, at the cost of computational power or time. The added flexibility means that we can decompose difference between groups in any outcome or and with any mediator (any variable type and distribution). See Sudharsanan & Bijlsma (2019) <doi:10.4054/MPIDR-WP-2019-004> for more information.

This package implements correlation structures for symmetric relational data (e.g. pairwise distances and dissimilarities) that interface with models using nlme'-style correlation structures. The maximum likelihood population effects method is described by Clarke et al. (2002) <doi:10.1198/108571102320>.

This package provides functions and data files to help CE Public-Use Microdata (PUMD) users calculate annual estimated expenditure means, standard errors, and quantiles according to the methods used by the CE with PUMD. For more information on the CE please visit <https://www.bls.gov/cex>. For further reading on CE estimate calculations please see the CE Calculation section of the U.S. Bureau of Labor Statistics (BLS) Handbook of Methods at <https://www.bls.gov/opub/hom/cex/calculation.htm>. For further information about CE PUMD please visit <https://www.bls.gov/cex/pumd.htm>.

Compute Chinese capital stocks in provinces level, based on Zhang (2008) <DOI:10.1080/14765280802028302>.

Change point tests for joint distributions and copulas using pseudo-observations with multipliers or bootstrap. The processes used here have been defined in Bucher, Kojadinovic, Rohmer & Segers <doi:10.1016/j.jmva.2014.07.012> and Nasri & Remillard <doi:10.1016/j.jmva.2019.03.002>.

Use the high-precision arithmetic provided by the R package Rmpfr to compute a custom-made Gauss quadrature nodes and weights, with up to 33 nodes, using a moment-based method via moment determinants. Paul Kabaila (2022) <arXiv:2211.04729>.

Check your R code for some of the most common layout flaws. Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8> in The C Programming Language did, be it explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in Clean Code: A Handbook of Agile Software Craftsmanship did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout <https://cran.r-project.org/package=lintr> instead.

Concordance probability estimate (CPE) is a commonly used performance measure in survival analysis that evaluates the predictive accuracy of a survival model. It measures how well a model can distinguish between pairs of individuals with different survival times. Specifically, it calculate the proportion of all pairs of individuals whose predicted survival times are correctly ordered.

Small package to clean the R console and the R environment with the call of just one function.

Interface to interest and foreign exchange rates published by the Czech National Bank.

Map functions while capturing results, errors, warnings, messages and other output tidily, then filter and summarise data frames or lists on the basis of those side effects.

Classical cryptography methods for words and brief phrases. Substitution, transposition and concealment (null) ciphers are available, like Caesar, Vigenère, Atbash, affine, simple substitution, Playfair, rail fence, Scytale, single column, bifid, trifid, and Polybius ciphers.

This package provides estimation procedures for copula-based stochastic frontier models for cross-sectional data. The package implements maximum likelihood estimation of stochastic frontier models allowing flexible dependence structures between inefficiency and noise terms through various copula families (e.g., Gaussian and Student-t). It enables estimation of technical efficiency scores, log-likelihood values, and information criteria (AIC and BIC). The implemented framework builds upon stochastic frontier analysis introduced by Aigner, Lovell and Schmidt (1977) <doi:10.1016/0304-4076(77)90052-5> and the copula theory described in Joe (2014, ISBN:9781466583221). Empirical applications of copula-based stochastic frontier models can be found in Wiboonpongse et al. (2015) <doi:10.1016/j.ijar.2015.06.001> and Maneejuk et al. (2017, ISBN:9783319562176).

Facilitate working with Statistics Canada (StatCan) Public Use Microdata Files (PUMF). Enables downloading of available PUMF data, parsing of metadata from command files or other sources to infer the layout structure, variable labels and value labels as well as missing data values, and returns a connection to a DuckDB database with the labelled data. Data and documentation come from Statistics Canada's Public Use Microdata Files <https://www.statcan.gc.ca/en/microdata/pumf>, distributed under the Statistics Canada Open Licence <https://www.statcan.gc.ca/en/terms-conditions/open-licence>.

Fits a Causal Effect Random Forest of Interaction Trees (CERFIT) which is a modification of the Random Forest algorithm where each split is chosen to maximize subgroup treatment heterogeneity. Doing this allows it to estimate the individualized treatment effect for each observation in either randomized controlled trial (RCT) or observational data. For more information see L. Li, R. A. Levine, and J. Fan (2022) <doi:10.1002/sta4.457>.

This package provides a big data version for fitting cumulative probability models using the orm() function from the rms package. See Liu et al. (2017) <DOI:10.1002/sim.7433> for details.