Color palettes generated from paintings.

An interactive document on the topic of basic probability using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://analyticmodels.shinyapps.io/BayesShiny/>.

Generates predicted stage change days for an insect, based on daily temperatures and development rate parameters, as developed by Pollard (2014) <http://mural.maynoothuniversity.ie/view/ethesisauthor/Pollard=3ACiaran_P=2E=3A=3A.html>. A few example datasets are included and implemented for P. vulgatissima, the blue willow beetle, but the approach can be readily applied to other species that display similar behaviour.

Identification, model fitting and estimation for time series with periodic structure. Additionally, procedures for simulation of periodic processes and real data sets are included. Hurd, H. L., Miamee, A. G. (2007) <doi:10.1002/9780470182833> Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994) <doi:10.1111/jtsa.12194> Brockwell, P. J., Davis, R. A. (1991, ISBN:978-1-4419-0319-8) Bretz, F., Hothorn, T., Westfall, P. (2010, ISBN: 9780429139543) Westfall, P. H., Young, S. S. (1993, ISBN:978-0-471-55761-6) Bloomfield, P., Hurd, H. L.,Lund, R. (1994) <doi:10.1111/j.1467-9892.1994.tb00181.x> Dehay, D., Hurd, H. L. (1994, ISBN:0-7803-1023-3) Vecchia, A. (1985) <doi:10.1080/00401706.1985.10488076> Vecchia, A. (1985) <doi:10.1111/j.1752-1688.1985.tb00167.x> Jones, R., Brelsford, W. (1967) <doi:10.1093/biomet/54.3-4.403> Makagon, A. (1999) <https://www.math.uni.wroc.pl/~pms/files/19.2/Article/19.2.5.pdf> Sakai, H. (1989) <doi:10.1111/j.1467-9892.1991.tb00069.x> Gladyshev, E. G. (1961) <https://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=dan&paperid=24851> Ansley (1979) <doi:10.1093/biomet/66.1.59> Hurd, H. L., Gerr, N. L. (1991) <doi:10.1111/j.1467-9892.1991.tb00088.x>.

Runs generalized and multinominal logistic (GLM and MLM) models, as well as random forest (RF), Bagging (BAG), and Boosting (BOOST). This package prints out to predictive outcomes easy for the selected data and data splits.

This package contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.

Small self-contained plots for use in larger plots or to delegate plotting in other functions. Also contains a number of alternative color palettes and HSL color space based tools to modify colors or palettes.

This package contains model fitting functions for linear and non-linear adsorption kinetic and diffusion models. Adsorption kinetics is used for characterizing the rate of solute adsorption and the time necessary for the adsorption process. Adsorption kinetics offers vital information on adsorption rate, adsorbent performance in response time, and mass transfer processes. In addition, diffusion models are included in the package as solute diffusion affects the adsorption kinetic experiments. This package consists of 20 adsorption and diffusion models, including Pseudo First Order (PFO), Pseudo Second Order (PSO), Elovich, and Weber-Morris model (commonly called the intraparticle model) stated by Plazinski et al. (2009) <doi:10.1016/j.cis.2009.07.009>. This package also contains a summary function where the statistical errors of each model are ranked for a more straightforward determination of the best fit model.

This package provides tools for cross-validated Lasso and Post-Lasso estimation. Built on top of the glmnet package by Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>, the main function plasso() extends the standard glmnet output with coefficient paths for Post-Lasso models, while cv.plasso() performs cross-validation for both Lasso and Post-Lasso models and different ways to select the penalty parameter lambda as discussed in Knaus (2021) <doi:10.1111/rssa.12623>.

Data for the extraterrestrial solar spectral irradiance and ground level solar spectral irradiance and irradiance. In addition data for shade light under vegetation and irradiance time series from different broadband sensors. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Fetches the PREDICTS database and relevant metadata from the Data Portal at the Natural History Museum, London <https://data.nhm.ac.uk>. Data were collated from over 400 existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from sites around the world. These data are described in Hudson et al. (2013) <doi:10.1002/ece3.2579>.

This package performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").

Calculate the optimal vertex partition of a graph using the persistence as objective function. These subroutines have been used in Avellone et al. <doi:10.1007/s10288-023-00559-z>.

This package provides functions and datasets to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).

Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.

The Prognostic Regression Offsets with Propagation of ERrors (for Treatment Effect Estimation) package facilitates direct adjustment for experiments and observational studies that is compatible with a range of study designs and covariance adjustment strategies. It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model.

Compilation and digitalization of the official registry of victims of state terrorism in Argentina during the last military coup. The original data comes from RUVTE-ILID (2019) <https://www.argentina.gob.ar/sitiosdememoria/ruvte/informe> and <http://basededatos.parquedelamemoria.org.ar/registros/>. The title, presentes, comes from present in spanish.

Visualize the partitions of simple decision trees, involving one or two predictors, on the scale of the original data. Provides an intuitive alternative to traditional tree diagrams, by visualizing how a decision tree divides the predictor space in a simple 2D plot alongside the original data. The parttree package supports both classification and regression trees from rpart and partykit', as well as trees produced by popular frontend systems like tidymodels and mlr3'. Visualization methods are provided for both base R graphics and ggplot2'.

This package provides a comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2017-18 Men questionnaire data for Punjab, Pakistan. The results of the present survey are critically important for the purposes of Sustainable Development Goals (SDGs) monitoring, as the survey produces information on 32 global Sustainable Development Goals (SDGs) indicators. The data was collected from 53,840 households selected at the second stage with systematic random sampling out of a sample of 2,692 clusters selected using probability proportional to size sampling. Six questionnaires were used in the survey: (1) a household questionnaire to collect basic demographic information on all de jure household members (usual residents), the household, and the dwelling; (2) a water quality testing questionnaire administered in three households in each cluster of the sample; (3) a questionnaire for individual women administered in each household to all women age 15-49 years; (4) a questionnaire for individual men administered in every second household to all men age 15-49 years; (5) an under-5 questionnaire, administered to mothers (or caretakers) of all children under 5 living in the household; and (6) a questionnaire for children age 5-17 years, administered to the mother (or caretaker) of one randomly selected child age 5-17 years living in the household (<http://www.mics.unicef.org/surveys>).

This package provides tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.

Generates random samples from the Polya-Gamma distribution using an implementation of the algorithm described in J. Windle's PhD thesis (2013) <https://repositories.lib.utexas.edu/bitstream/handle/2152/21842/WINDLE-DISSERTATION-2013.pdf>. The underlying implementation is in C.

Generate Mermaid syntax for a pedigree flowchart from a pedigree data frame. Mermaid syntax is commonly used to generate plots, charts, diagrams, and flowcharts. It is a textual syntax for creating reproducible illustrations. This package generates Mermaid syntax from a pedigree data frame to visualize a pedigree flowchart. The Mermaid syntax can be embedded in a Markdown or R Markdown file, or viewed on Mermaid editors and renderers. Links shape, style, and orientation can be customized via function arguments, and nodes shapes and styles can be customized via optional columns in the pedigree data frame.