This package provides classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.

Sample size calculations in causal inference with observational data are increasingly desired. This package is a tool to calculate sample size under prespecified power with minimal summary quantities needed.

The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

This package provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL). See Piotr Sobczyk, Malgorzata Bogdan, Julie Josse "Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood" (2017) <doi:10.1080/10618600.2017.1340302>.

This package provides a profile boosting framework for feature selection in parametric models. It offers a unified interface pboost() and several wrapped models, including linear model, generalized linear models, quantile regression, Cox proportional hazards model, beta regression. An S3 interface EBIC() is provided as the stopping rule for the profile boosting by default.

Management problems of deterministic and stochastic projects. It obtains the duration of a project and the appropriate slack for each activity in a deterministic context. In addition it obtains a schedule of activities time (Castro, Gómez & Tejada (2007) <doi:10.1016/j.orl.2007.01.003>). It also allows the management of resources. When the project is done, and the actual duration for each activity is known, then it can know how long the project is delayed and make a fair delivery of the delay between each activity (Bergantiños, Valencia-Toledo & Vidal-Puga (2018) <doi:10.1016/j.dam.2017.08.012>). In a stochastic context it can estimate the average duration of the project and plot the density of this duration, as well as, the density of the early and last times of the chosen activities. As in the deterministic case, it can make a distribution of the delay generated by observing the project already carried out.

Parsimonious Ultrametric Gaussian Mixture Models via grouped coordinate ascent (equivalent to EM) algorithm characterized by the inspection of hierarchical relationships among variables via parsimonious extended ultrametric covariance structures. The methodologies are described in Cavicchia, Vichi, Zaccaria (2024) <doi:10.1007/s11222-024-10405-9>, (2022) <doi:10.1007/s11634-021-00488-x> and (2020) <doi:10.1007/s11634-020-00400-z>.

Reads/write binary genotype file compatible with PLINK <https://www.cog-genomics.org/plink/1.9/input#bed> into/from a R matrix; traverse genotype data one windows of variants at a time, like apply() or a for loop; reads/writes genotype relatedness/kinship matrices created by PLINK <https://www.cog-genomics.org/plink/1.9/distance#make_rel> or GCTA <https://cnsgenomics.com/software/gcta/#MakingaGRM> into/from a R square matrix. It is best used for bringing data produced by PLINK and GCTA into R workflow.

Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.

Estimates (and controls for) phylogenetic signal through phylogenetic eigenvectors regression (PVR) and phylogenetic signal-representation (PSR) curve, along with some plot utilities.

Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.

This package provides functions to simplify the PatentsView API (<https://search.patentsview.org/docs/docs/Search%20API/SearchAPIReference/#api-query-language>) query language, send GET and POST requests to the API's twenty seven endpoints, and parse the data that comes back.

This package provides a collection of R Markdown templates for creating simple and easy to personalize single page websites.

This package contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active. The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification br or a denoting bedrest/sleep and activity, respectively. The package is designed to be used after wear/nonwear marking function in the PhysicalActivity package. Version 1.1 adds preschool thresholds and corrects for possible errors in algorithm implementation.

This package provides a method of clustering functional data using subregion information of the curves. It is intended to supplement the fda and fda.usc packages in functional data object clustering. It also facilitates the printing and plotting of the results in a tree format and limits the partitioning candidates into a specific set of subregions.

This package implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.

Markov chain Monte Carlo diagnostic plots. The purpose of the package is to combine existing tools from the coda and lattice packages, and make it easy to adjust graphical details.

Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, PKbioanalysis provides a relational infrastructure for plate management and injection sequence.

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>.

Support functions, data sets, and vignettes for the psych package. Contains several of the biggest data sets for the psych package as well as four vignettes. A few helper functions for file manipulation are included as well. For more information, see the <https://personality-project.org/r/> web page.

This package provides functions to estimate the incubation period distribution of post-infectious syndrome which is defined as the time between the symptom onset of the antecedent infection and that of the post-infectious syndrome.

Calculate common types of tables for weighted survey data. Options include topline and (2-way and 3-way) crosstab tables of categorical or ordinal data as well as summary tables of weighted numeric variables. Optionally, include the margin of error at selected confidence intervals including the design effect. The design effect is calculated as described by Kish (1965) <doi:10.1002/bimj.19680100122> beginning on page 257. Output takes the form of tibbles (simple data frames). This package conveniently handles labelled data, such as that commonly used by Stata and SPSS. Complex survey design is not supported at this time.

See Miroshnikov and Conlon (2014) <doi:10.1371/journal.pone.0108425>. Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.