Features unstructured, structured and reverse geocoding using the photon geocoding API <https://photon.komoot.io/>. Facilitates the setup of local photon instances to enable offline geocoding.

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around <https://postcodes.io>.

Set of functions to polish content for Microsoft Word and PowerPoint into OOXML'. Polishing is the conversion of the R object into an OOXML representation of the object that can then be added to Word or PowerPoint files.

This package provides an R interface to the PCATS API <https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit tasks and retrieve results.

Periodic B Splines Basis.

Calculate POTH for treatment hierarchies from frequentist and Bayesian network meta-analysis. POTH quantifies the certainty in a treatment hierarchy. Subset POTH, POTH residuals, and best k treatments POTH can also be calculated to improve interpretation of treatment hierarchies.

An enterprise-targeted scalable and UI-standardized shiny framework including a variety of developer convenience functions with the goal of both streamlining robust application development while assisting with creating a consistent user experience regardless of application or developer.

Use phenotype risk scores based on linked clinical and genetic data to study Mendelian disease and rare genetic variants. See Bastarache et al. 2018 <doi:10.1126/science.aal4043>.

The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Considering the singly imputed synthetic data generated via plug-in sampling under the multivariate normal model, draws inference procedures including the generalized variance, the sphericity test, the test for independence between two subsets of variables, and the test for the regression of one set of variables on the other. For more details see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>.

This package implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models.

This package provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al. (2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et al. (2005) <doi:10.1198/004017004000000563>.

Fit penalized splines mixed-effects models (a special case of additive models) for large longitudinal datasets. The package includes a psme() function that (1) relies on package mgcv for constructing population and subject smooth functions as penalized splines, (2) transforms the constructed additive model to a linear mixed-effects model, (3) exploits package lme4 for model estimation and (4) backtransforms the estimated linear mixed-effects model to the additive model for interpretation and visualization. See Pedersen et al. (2019) <doi:10.7717/peerj.6876> and Bates et al. (2015) <doi:10.18637/jss.v067.i01> for an introduction. Unlike the gamm() function in mgcv', the psme() function is fast and memory-efficient, able to handle datasets with millions of observations.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

Leading/lagging a panel, creating dummy variables, taking panel differences, looking for panel autocorrelations, and more. Implemented via a data.table back end.

Estimates unsupervised outlier probabilities for multivariate numeric data with many observations from a nonparametric outlier statistic.

This package provides a collection of scripts and data files for the statistics text: "Process Improvement using Data" <https://learnche.org/pid/> and the online course "Experimentation for Improvement" found on Coursera. The package contains code for designed experiments, data sets and other convenience functions used in the book.

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

Performance metric provides different performance measures like mean squared error, root mean square error, mean absolute deviation, mean absolute percentage error etc. of a fitted model. These can provide a way for forecasters to quantitatively compare the performance of competing models. For method details see (i) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see <doi:10.18637/jss.v107.i07>.

Understanding the dynamics of potentially heterogeneous variables is important in statistical applications. This package provides tools for estimating the degree of heterogeneity across cross-sectional units in the panel data analysis. The methods are developed by Okui and Yanagi (2019) <doi:10.1016/j.jeconom.2019.04.036> and Okui and Yanagi (2020) <doi:10.1093/ectj/utz019>.

Facilitates the testing of causal relationships among lineage-pair traits in a phylogenetically informed context. Lineage-pair traits are characters that are defined for pairs of lineages instead of individual taxa. Examples include the strength of reproductive isolation, range overlap, competition coefficient, diet niche similarity, and relative hybrid fitness. Users supply a lineage-pair dataset and a phylogeny. phylopairs calculates a covariance matrix for the pairwise-defined data and provides built-in models to test for relationships among variables while taking this covariance into account. Bayesian sampling is run through built-in Stan programs via the rstan package. The various models and methods that this package makes available are described in Anderson et al. (In Review), Coyne and Orr (1989) <doi:10.1111/j.1558-5646.1989.tb04233.x>, Fitzpatrick (2002) <doi:10.1111/j.0014-3820.2002.tb00860.x>, and Castillo (2007) <doi:10.1002/ece3.3093>.

This package provides functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details.