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r-prediction
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A one-function package containing 'prediction()', a type-safe alternative to 'predict()' that always returns a data frame. The 'summary()' method provides a data frame with average predictions, possibly over counterfactual versions of the data (a la the 'margins' command in 'Stata'). Marginal effect estimation is provided by the related package, 'margins' <https://cran.r-project.org/package=margins>. The package currently supports common model types (e.g., "lm", "glm") from the 'stats' package, as well as numerous other model classes from other add-on packages. See the README or main package documentation page for a complete listing.
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2024-01-16 |
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r-praise
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Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.
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2024-01-16 |
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r-pracma
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Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.
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2024-01-16 |
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r-practools
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Functions and datasets to support Valliant, Dever, and Kreuter, Practical Tools for Designing and Weighting Survey Samples (2nd edition, 2018). Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs, and single-stage audit sample designs. Functions are included that will group geographic units accounting for distances apart and measures of size. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example data sets are included.
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2024-01-16 |
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r-prais
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The Prais-Winsten estimator (Prais & Winsten, 1954) takes into account AR(1) serial correlation of the errors in a linear regression model. The procedure recursively estimates the coefficients and the error autocorrelation of the specified model until sufficient convergence of the AR(1) coefficient is attained.
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2024-01-16 |
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r-prabclus
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Distance-based parametric bootstrap tests for clustering with spatial neighborhood information. Some distance measures, Clustering of presence-absence, abundance and multilocus genetic data for species delimitation, nearest neighbor based noise detection. Genetic distances between communities. Tests whether various distance-based regressions are equal. Try package?prabclus for on overview.
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2024-01-16 |
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r-pqantimalarials
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This package allows users to calculate the number of under-five child deaths caused by consumption of poor quality antimalarials across 39 sub-Saharan nations. The package supports one function, that starts an interactive web tool created using the shiny R package. The web tool runs locally on the user's machine. The web tool allows users to set input parameters (prevalence of poor quality antimalarials, case fatality rate of children who take poor quality antimalarials, and sample size) which are then used to perform an uncertainty analysis following the Latin hypercube sampling scheme. Users can download the output figures as PDFs, and the output data as CSVs. Users can also download their input parameters for reference. This package was designed to accompany the analysis presented in: J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan "Estimated under-five deaths associated with poor-quality antimalarials in sub-Saharan Africa", 2014. Paper submitted.
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2024-01-16 |
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r-pps
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Functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.
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2024-01-16 |
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r-potools
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Translating messages in R packages is managed using the po top-level directory and the 'gettext' program. This package provides some helper functions for building this support in R packages, e.g. common validation & I/O tasks.
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2024-01-16 |
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r-ppcor
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Calculates partial and semi-partial (part) correlations along with p-value.
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2024-01-16 |
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r-powerupr
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Includes tools to calculate statistical power, minimum detectable effect size (MDES), MDES difference (MDESD), and minimum required sample size for various multilevel randomized experiments (MRE) with continuous outcomes. Accomodates 14 types of MRE designs to detect main treatment effect, seven types of MRE designs to detect moderated treatment effect (2-1-1, 2-1-2, 2-2-1, 2-2-2, 3-3-1, 3-3-2, and 3-3-3 designs; <total.lev> - <trt.lev> - <mod.lev>), five types of MRE designs to detect mediated treatment effects (2-1-1, 2-2-1, 3-1-1, 3-2-1, and 3-3-1 designs; <trt.lev> - <med.lev> - <out.lev>), four types of partially nested (PN) design to detect main treatment effect, and three types of PN designs to detect mediated treatment effects (2/1, 3/1, 3/2; <trt.arm.lev> / <ctrl.arm.lev>). See 'PowerUp!' Excel series at <https://www.causalevaluation.org/>.
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2024-01-16 |
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r-powersurvepi
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Functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.
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2024-01-16 |
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r-portes
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Contains common univariate and multivariate portmanteau test statistics for time series models. These tests are based on using asymptotic distributions such as chi-square distribution and based on using the Monte Carlo significance tests. Also, it can be used to simulate from univariate and multivariate seasonal time series models.
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2024-01-16 |
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r-powerpkg
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There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. These are based on a Mathematica notebook created by Martin Farrall. (2) To examine how the power of the transmission disequilibrium test (TDT) depends on the disease allele frequency, the marker allele frequency, the strength of the linkage disequilibrium, and the magnitude of the genetic effect. We will use an R program that implements the power formulae of Abel and Muller-Myhsok (1998). These formulae allow one to quickly compute power of the TDT approach under a variety of different conditions. This R program was modeled on Martin Farrall's Mathematica notebook.
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2024-01-16 |
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r-powernormal
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Miscellaneous functions for a descriptive analysis and initial Bayesian and classical inference for the power parameter of the the Power Normal (PN) distribution. This miscellaneous will be extend for more distributions into the power family and the three-parameter model.
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2024-01-16 |
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r-powerlaw
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An implementation of maximum likelihood estimators for a variety of heavy tailed distributions, including both the discrete and continuous power law distributions. Additionally, a goodness-of-fit based approach is used to estimate the lower cut-off for the scaling region.
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2024-01-16 |
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r-powermediation
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Functions to calculate power and sample size for testing (1) mediation effects; (2) the slope in a simple linear regression; (3) odds ratio in a simple logistic regression; (4) mean change for longitudinal study with 2 time points; (5) interaction effect in 2-way ANOVA; and (6) the slope in a simple Poisson regression.
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2024-01-16 |
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r-portfoliobacktest
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Automated backtesting of multiple portfolios over multiple datasets of stock prices in a rolling-window fashion. Intended for researchers and practitioners to backtest a set of different portfolios, as well as by a course instructor to assess the students in their portfolio design in a fully automated and convenient manner, with results conveniently formatted in tables and plots. Each portfolio design is easily defined as a function that takes as input a window of the stock prices and outputs the portfolio weights. Multiple portfolios can be easily specified as a list of functions or as files in a folder. Multiple datasets can be conveniently extracted randomly from different markets, different time periods, and different subsets of the stock universe. The results can be later assessed and ranked with tables based on a number of performance criteria (e.g., expected return, volatility, Sharpe ratio, drawdown, turnover rate, return on investment, computational time, etc.), as well as plotted in a number of ways with nice barplots and boxplots.
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2024-01-16 |
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r-powercomprisk
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A power analysis tool for jointly testing the cause-1 cause-specific hazard and the any-cause hazard with competing risks data.
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2024-01-16 |
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r-powdist
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Density, distribution function, quantile function and random generation for the family of power and reversal power distributions.
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2024-01-16 |
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r-postlogic
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Provides adds postfix and infix logic operators for if, then, unless, and otherwise.
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2024-01-16 |
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r-postinfectious
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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.
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2024-01-16 |
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r-posterior
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Provides useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to: (a) Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions. (b) Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws. (c) Provide various summaries of draws in convenient formats. (d) Provide lightweight implementations of state of the art posterior inference diagnostics. References: Vehtari et al. (2021) <doi:10.1214/20-BA1221>.
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2024-01-16 |
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r-postcodesior
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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>.
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2024-01-16 |
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r-posi
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In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.
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2024-01-16 |
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r-popgenreport
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Provides beginner friendly framework to analyse population genetic data. Based on 'adegenet' objects it uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit <http://www.popgenreport.org/>.
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2024-01-16 |
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r-portsort
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Designed to aid both academic researchers and asset managers in conducting factor based portfolio sorts. Provides functionality to sort assets into portfolios for up to three factors via a conditional or unconditional sorting procedure.
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2024-01-16 |
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r-portfolio
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Classes for analysing and implementing equity portfolios, including routines for generating tradelists and calculating exposures to user-specified risk factors.
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2024-01-16 |
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r-poptrend
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Functions to estimate and plot smooth or linear population trends, or population indices, from animal or plant count survey data.
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2024-01-16 |
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r-popkin
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Provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index). Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.
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2024-01-16 |
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r-poorman
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A replication of key functionality from 'dplyr' and the wider 'tidyverse' using only 'base'.
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2024-01-16 |
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r-popreconstruct
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Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
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2024-01-16 |
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r-popdemo
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Tools for modelling populations and demography using matrix projection models, with deterministic and stochastic model implementations. Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools.
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2024-01-16 |
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r-popepi
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Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of 'Lexis' objects (from package 'Epi') and other computations achieved using 'data.table'.
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2024-01-16 |
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r-popbio
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Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
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2024-01-16 |
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r-polychrome
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Tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) <doi:10.18637/jss.v090.c01>.
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2024-01-16 |
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r-pop
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Population dynamic models underpin a range of analyses and applications in ecology and epidemiology. The various approaches for analysing population dynamics models (MPMs, IPMs, ODEs, POMPs, PVA) each require the model to be defined in a different way. This makes it difficult to combine different modelling approaches and data types to solve a given problem. 'pop' aims to provide a flexible and easy to use common interface for constructing population dynamic models and enabling to them to be fitted and analysed in lots of different ways.
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2024-01-16 |
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r-pooledmeangroup
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Calculates the pooled mean group (PMG) estimator for dynamic panel data models, as described by Pesaran, Shin and Smith (1999) <doi:10.1080/01621459.1999.10474156>.
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2024-01-16 |
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r-pool
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Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are 'DBI' connections.
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2024-01-16 |
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r-pomic
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Calculations of an information criterion are proposed to check the quality of simulations results of Agent-based models (ABM/IBM) or other non-linear rule-based models. The POMDEV measure (Pattern Oriented Modelling DEViance) is based on the Kullback-Leibler divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores (Pattern Oriented Modelling Information Criterion) can be calculated. This method could be further developed to incorporate multiple patterns assessment. Piou C, U Berger and V Grimm (2009) <doi:10.1016/j.ecolmodel.2009.05.003>.
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2024-01-16 |
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r-polytrend
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This algorithm classifies the trends into linear, quadratic, cubic, concealed and no-trend types. The "concealed trends" are those trends that possess quadratic or cubic forms, but the net change from the start of the time period to the end of the time period hasn't been significant. The "no-trend" category includes simple linear trends with statistically in-significant slope coefficient.
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2024-01-16 |
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r-pollster
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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.
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2024-01-16 |
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r-polysegratio
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Perform classic chi-squared tests and Ripol et al(1999) binomial confidence interval approach for autopolyploid dominant markers. Also, dominant markers may be generated for families of offspring where either one or both of the parents possess the marker. Missing values and misclassified markers may be generated at random.
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2024-01-16 |
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r-polite
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Be responsible when scraping data from websites by following polite principles: introduce yourself, ask for permission, take slowly and never ask twice.
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2024-01-16 |
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r-polypatex
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Functions to perform paternity exclusion via allele matching, in autopolyploid species having ploidy 4, 6, or 8. The marker data used can be genotype data (copy numbers known) or 'allelic phenotype data' (copy numbers not known).
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2024-01-16 |
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r-polynom
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A collection of functions to implement a class for univariate polynomial manipulations.
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2024-01-16 |
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r-polycor
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Computes polychoric and polyserial correlations by quick "two-step" methods or ML, optionally with standard errors; tetrachoric and biserial correlations are special cases.
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2024-01-16 |
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r-polyaaeppli
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Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution. More information on the implementation can be found at Conrad J. Burden (2014) <arXiv:1406.2780>.
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2024-01-16 |
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r-poliscidata
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Bundles the datasets and functions used in the textbook by Philip Pollock and Barry Edwards, an R Companion to Essentials of Political Analysis, Second Edition.
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2024-01-16 |
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r-pointblank
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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.
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2024-01-16 |
