This package contains functions for data preparation, prediction of transition probabilities, estimating semi-parametric regression models and for implementing nonparametric estimators for other quantities. See Meira-Machado and Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.

Utilizes the lme4 and optimx packages (previously the optim() function from stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>. Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.

The constructs used to study the human psychology have many definitions and corresponding instructions for eliciting and coding qualitative data pertaining to constructs content and for measuring the constructs. This plethora of definitions and instructions necessitates unequivocal reference to specific definitions and instructions in empirical and secondary research. This package implements a human- and machine-readable standard for specifying construct definitions and instructions for measurement and qualitative research based on YAML'. This standard facilitates systematic unequivocal reference to specific construct definitions and corresponding instructions in a decentralized manner (i.e. without requiring central curation; Peters (2020) <doi:10.31234/osf.io/xebhn>).

Computes the minimum sample size required for the development of a new multivariable prediction model using the criteria proposed by Riley et al. (2018) <doi: 10.1002/sim.7992>. pmsampsize can be used to calculate the minimum sample size for the development of models with continuous, binary or survival (time-to-event) outcomes. Riley et al. (2018) <doi: 10.1002/sim.7992> lay out a series of criteria the sample size should meet. These aim to minimise the overfitting and to ensure precise estimation of key parameters in the prediction model.

Package to Percentile estimation of fetal weight for twins by chorionicity (dichorionic-diamniotic or monochorionic-diamniotic).

Calculate parametric mortality and Fertility models, following packages BaSTA in Colchero, Jones and Rebke (2012) <doi:10.1111/j.2041-210X.2012.00186.x> and BaFTA <https://github.com/fercol/BaFTA>, summary statistics (e.g. ageing rates, life expectancy, lifespan equality, etc.), life table and product limit estimators from census data.

An R package for polygenic trait analysis.

This package provides a set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) datasets inside the pharmaverse family of packages. SDTM dataset specifications are described in the CDISC SDTM implementation guide, accessible by creating a free account on <https://www.cdisc.org/>.

Deploy, maintain, and invoke predictive models using the Alteryx Promote REST API. Alteryx Promote is available at the URL: <https://www.alteryx.com/products/alteryx-promote>.

Derives prediction rule ensembles (PREs). Largely follows the procedure for deriving PREs as described in Friedman & Popescu (2008; <DOI:10.1214/07-AOAS148>), with adjustments and improvements described in Fokkema (2020; <DOI:10.18637/jss.v092.i12>) and Fokkema & Strobl (2020; <DOI:10.1037/met0000256>). The main function pre() derives prediction rule ensembles consisting of rules and/or linear terms for continuous, binary, count, multinomial, survival and multivariate continuous responses. Function gpe() derives generalized prediction ensembles, consisting of rules, hinge and linear functions of the predictor variables.

The name of the package is derived from the French, pour ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).

Given an arbitrary set of spatial regions and road networks, generate a set of representative points, or pseudohouseholds, that can be used for travel burden analysis. Parallel processing is supported.

Several person-fit statistics (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) are offered. These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors.

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.

Eco-phylogenetic and community phylogenetic analyses. Keeps community ecological and phylogenetic data matched up and comparable using comparative.comm objects. Wrappers for common community phylogenetic indices ('pez.shape', pez.evenness', pez.dispersion', and pez.dissimilarity metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', sim.meta.comm').

Generates Plus Code of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by Google Inc. present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.

This package provides functions to estimate and plot smooth or linear population trends, or population indices, from animal or plant count survey data.

Send push notifications to mobile devices or the desktop using Pushover <https://pushover.net>. These notifications can display things such as results, job status, plots, or any other text or numeric data.

This package provides functions representing some useful empirical and data-driven models of heat loss, corrosion diagnostics, reliability and predictive maintenance of pipeline systems. The package is an option for technical engineering departments of heat generating and heat transfer companies that use or plan to use regulatory calculations in their activities. Methods are described in Timashev et al. (2016) <doi:10.1007/978-3-319-25307-7>, A.C.Reddy (2017) <doi:10.1016/j.matpr.2017.07.081>, Minenergo (2008) <https://docs.cntd.ru/document/902148459>, Minenergo (2005) <https://docs.cntd.ru/document/1200035568>, Xing LU. (2014) <doi:10.1080/23744731.2016.1258371>.

PubTator <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. PubTator offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package pubtatordb provides a set of functions that make it easy for the average R user to download PubTator annotations, create, and then query a local version of the database.

This package provides tools for both single and batch image manipulation and analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery.

Validate data in data frames, tibble objects, Spark DataFrames', and database tables. Validation pipelines can be made using easily-readable, consecutive validation steps. Upon execution of the validation plan, several reporting options are available. User-defined thresholds for failure rates allow for the determination of appropriate reporting actions. Many other workflows are available including an information management workflow, where the aim is to record, collect, and generate useful information on data tables.

Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

The function pointdensity returns a density count and the temporal average for every point in the original list. The dataframe returned includes four columns: lat, lon, count, and date_avg. The "lat" column is the original latitude data; the "lon" column is the original longitude data; the "count" is the density count of the number of points within a radius of radius*grid_size (the neighborhood); and the date_avg column includes the average date of each point in the neighborhood.