Implementation of the Control Polygon Reduction and Control Net Reduction methods for finding parsimonious B-spline regression models.

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.

This package provides functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.

Provided R functions for working with the Conditional Negative Binomial distribution.

The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the lme4 package and robust estimators from the rlmer() function in the robustlmm package, as well as the varComprob() function in the robustvarComp package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

An integrated set of tools for thermodynamic calculations in aqueous geochemistry and geobiochemistry. Functions are provided for writing balanced reactions to form species from user-selected basis species and for calculating the standard molal properties of species and reactions, including the standard Gibbs energy and equilibrium constant. Calculations of the non-equilibrium chemical affinity and equilibrium chemical activity of species can be portrayed on diagrams as a function of temperature, pressure, or activity of basis species; in two dimensions, this gives a maximum affinity or predominance diagram. The diagrams have formatted chemical formulas and axis labels, and water stability limits can be added to Eh-pH, oxygen fugacity- temperature, and other diagrams with a redox variable. The package has been developed to handle common calculations in aqueous geochemistry, such as solubility due to complexation of metal ions, mineral buffers of redox or pH, and changing the basis species across a diagram ("mosaic diagrams"). CHNOSZ also implements a group additivity algorithm for the standard thermodynamic properties of proteins.

Quick and easy access to datasets that let you replicate the empirical examples in Cameron and Trivedi (2005) "Microeconometrics: Methods and Applications" (ISBN: 9780521848053).The data are available as soon as you install and load the package (lazy-loading) as data frames. The documentation includes reference to chapter sections and page numbers where the datasets are used.

This package implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.

Balance sheet and income statement metrics, investment analysis methods, valuation methods, loan amortization schedules, and Capital Asset Pricing Model.

This package provides functions to perform comparative causal mediation analysis to compare the mediation effects of different treatments via a common mediator. Results contain the estimates and confidence intervals for the two comparative causal mediation analysis estimands, as well as the ATE and ACME for each treatment. Functions provided in the package will automatically assess the comparative causal mediation analysis scope conditions (i.e. for each comparative causal mediation estimand, a numerator and denominator that are both estimated with the desired statistical significance and of the same sign). Results will be returned for each comparative causal mediation estimand only if scope conditions are met for it. See details in Bansak(2020)<doi:10.1017/pan.2019.31>.

This package performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550>.

Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.

Package for the analysis of categorical functional data. The main purpose is to compute an encoding (real functional variable) for each state <doi:10.3390/math9233074>. It also provides functions to perform basic statistical analysis on categorical functional data.

This package provides a tidied subset of the US College Scorecard dataset, containing institutional characteristics, enrollment, student aid, costs, and student outcomes at institutions of higher education in the United States.

Race results of the Cherry Blossom Run, which is an annual road race that takes place in Washington, DC.

This package provides functions for efficient computation of non-linear spatial predictions with local change of support (Hofer, C. and Papritz, A. (2011) "constrainedKriging: An R-package for customary, constrained and covariance-matching constrained point or block kriging" <doi:10.1016/j.cageo.2011.02.009>). This package supplies functions for two-dimensional spatial interpolation by constrained (Cressie, N. (1993) "Aggregation in geostatistical problems" <doi:10.1007/978-94-011-1739-5_3>), covariance-matching constrained (Aldworth, J. and Cressie, N. (2003) "Prediction of nonlinear spatial functionals" <doi:10.1016/S0378-3758(02)00321-X>) and universal (external drift) Kriging for points or blocks of any shape from data with a non-stationary mean function and an isotropic weakly stationary covariance function. The linear spatial interpolation methods, constrained and covariance-matching constrained Kriging, provide approximately unbiased prediction for non-linear target values under change of support. This package extends the range of tools for spatial predictions available in R and provides an alternative to conditional simulation for non-linear spatial prediction problems with local change of support.

Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.

This package implements the algorithm described in Trapnell,C. et al. (2010) <doi: 10.1038/nbt.1621>. This function takes read counts matrix of RNA-Seq data, feature lengths which can be retrieved using biomaRt package, and the mean fragment lengths which can be calculated using the CollectInsertSizeMetrics(Picard) tool. It then returns a matrix of FPKM normalised data by library size and feature effective length. It also provides the user with a quick and reliable function to generate FPKM heatmap plot of the highly variable features in RNA-Seq dataset.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.

Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a shiny interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the Certara.RsNLME package. Additional support to generate the underlying Certara.RsNLME code to recreate the corresponding model in R is provided in the user interface.

This package provides harmonized and non-harmonized population pyramid datasets from the Indonesian population censuses (1971â 2020), along with tools for visualization and an interactive shiny'-based explorer application. Data are processed from IPUMS International (1971â 2010) and the Population Census 2020 (BPS Indonesia).

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.

Detect and quantify community assembly processes using trait values of individuals or populations, the T-statistics and other metrics, and dedicated null models.