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easystats

datawizard:Easy Data Wrangling and Statistical Transformations

A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.

Maintained by Etienne Bacher. Last updated 2 days ago.

datadplyrhacktoberfestjanitormanipulationreshapetidyrwrangling

223 stars 14.77 score 436 scripts 120 dependents

matthias-da

robCompositions:Compositional Data Analysis

Methods for analysis of compositional data including robust methods (<doi:10.1007/978-3-319-96422-5>), imputation of missing values (<doi:10.1016/j.csda.2009.11.023>), methods to replace rounded zeros (<doi:10.1080/02664763.2017.1410524>, <doi:10.1016/j.chemolab.2016.04.011>, <doi:10.1016/j.csda.2012.02.012>), count zeros (<doi:10.1177/1471082X14535524>), methods to deal with essential zeros (<doi:10.1080/02664763.2016.1182135>), (robust) outlier detection for compositional data, (robust) principal component analysis for compositional data, (robust) factor analysis for compositional data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, functional data analysis (<doi:10.1016/j.csda.2015.07.007>) and p-splines (<doi:10.1016/j.csda.2015.07.007>), contingency (<doi:10.1080/03610926.2013.824980>) and compositional tables (<doi:10.1111/sjos.12326>, <doi:10.1111/sjos.12223>, <doi:10.1080/02664763.2013.856871>) and (robust) Anderson-Darling normality tests for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR, and their inverse transformations). In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the ternary diagram.

Maintained by Matthias Templ. Last updated 1 months ago.

cpp

11 stars 9.21 score 226 scripts 2 dependents

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 8 days ago.

fisheriesflrfisheries-modelling

16 stars 8.78 score 956 scripts 23 dependents

eddelbuettel

RQuantLib:R Interface to the 'QuantLib' Library

The 'RQuantLib' package makes parts of 'QuantLib' accessible from R The 'QuantLib' project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.

Maintained by Dirk Eddelbuettel. Last updated 3 days ago.

cppquantlibcpp

123 stars 8.44 score 194 scripts

finnishcancerregistry

popEpi:Functions for Epidemiological Analysis using Population Data

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'.

Maintained by Joonas Miettinen. Last updated 2 months ago.

adjust-estimatesage-adjustingdirect-adjustingepidemiologyindirect-adjustingsurvival

8 stars 8.05 score 117 scripts 1 dependents

eheinzen

elo:Ranking Teams by Elo Rating and Comparable Methods

A flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, 'Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models.

Maintained by Ethan Heinzen. Last updated 1 years ago.

eloelo-ratinglogistic-regressionmarkov-chainmarkov-modelrankingsports-analyticscpp

37 stars 7.05 score 153 scripts

leifeld

btergm:Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood

Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood. Goodness of fit assessment for ERGMs, TERGMs, and SAOMs. Micro-level interpretation of ERGMs and TERGMs. The methods are described in Leifeld, Cranmer and Desmarais (2018), JStatSoft <doi:10.18637/jss.v083.i06>.

Maintained by Philip Leifeld. Last updated 10 days ago.

complex-networksdynamic-analysisergmestimationgoodness-of-fitinferencelongitudinal-datanetwork-analysispredictiontergm

18 stars 7.03 score 83 scripts 2 dependents

mjskay

ggblend:Blending and Compositing Algebra for 'ggplot2'

Algebra of operations for blending, copying, adjusting, and compositing layers in 'ggplot2'. Supports copying and adjusting the aesthetics or parameters of an existing layer, partitioning a layer into multiple pieces for re-composition, applying affine transformations to layers, and combining layers (or partitions of layers) using blend modes (including commutative blend modes, like multiply and darken). Blend mode support is particularly useful for creating plots with overlapping groups where the layer drawing order does not change the output; see Kindlmann and Scheidegger (2014) <doi:10.1109/TVCG.2014.2346325>.

Maintained by Matthew Kay. Last updated 2 years ago.

186 stars 6.30 score 71 scripts 1 dependents

qlcal

qlcal:R Bindings to the Calendaring Functionality of 'QuantLib'

'QuantLib' bindings are provided for R using 'Rcpp' via an evolved version of the initial header-only 'Quantuccia' project offering an subset of 'QuantLib' (now maintained separately just for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to 'QuantLib' (and hence also 'Quantuccia').

Maintained by Dirk Eddelbuettel. Last updated 1 months ago.

cpp

7 stars 4.99 score 28 scripts

eddelbuettel

RcppQuantuccia:R Bindings to the Calendaring Functionality of 'QuantLib'

'QuantLib' bindings are provided for R using 'Rcpp' via an updated variant of the header-only 'Quantuccia' project (put together initially by Peter Caspers) offering an essential subset of 'QuantLib' (and now maintained separately for the calendaring subset). See the included file 'AUTHORS' for a full list of contributors to both 'QuantLib' and 'Quantuccia'.

Maintained by Dirk Eddelbuettel. Last updated 4 months ago.

cpp11quantlibcpp

12 stars 4.62 score 23 scripts

devopifex

g2r:Interactive Grammar of Graphics

Interactive grammar of graphics.

Maintained by John Coene. Last updated 3 years ago.

visualization

119 stars 4.53 score 57 scripts

cnuge

debar:A Post-Clustering Denoiser for COI-5P Barcode Data

The 'debar' sequence processing pipeline is designed for denoising high throughput sequencing data for the animal DNA barcode marker cytochrome c oxidase I (COI). The package is designed to detect and correct insertion and deletion errors within sequencer outputs. This is accomplished through comparison of input sequences against a profile hidden Markov model (PHMM) using the Viterbi algorithm (for algorithm details see Durbin et al. 1998, ISBN: 9780521629713). Inserted base pairs are removed and deleted base pairs are accounted for through the introduction of a placeholder character. Since the PHMM is a probabilistic representation of the COI barcode, corrections are not always perfect. For this reason 'debar' censors base pairs adjacent to reported indel sites, turning them into placeholder characters (default is 7 base pairs in either direction, this feature can be disabled). Testing has shown that this censorship results in the correct sequence length being restored, and erroneous base pairs being masked the vast majority of the time (>95%).

Maintained by Cameron M. Nugent. Last updated 1 years ago.

bioinformaticsdenoisingdna-barcodingdna-sequencinghidden-markov-modelmachine-learning

1 stars 4.00 score 8 scripts

markvanderloo

rspa:Adapt Numerical Records to Fit (in)Equality Restrictions

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the 'validate' package. The core algorithms have recently been moved to the 'lintools' package, refer to 'lintools' for a more basic interface and access to a version of the algorithm that works with sparse matrices.

Maintained by Mark van der Loo. Last updated 10 months ago.

3 stars 3.45 score 19 scripts

olangsrud

ffmanova:Fifty-Fifty MANOVA

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

Maintained by Øyvind Langsrud. Last updated 1 years ago.

2 stars 3.00 score 7 scripts

leifeld

xergm.common:Common Infrastructure for Extensions of Exponential Random Graph Models

Datasets and definitions of generic functions used in dependencies of the 'xergm' package.

Maintained by Philip Leifeld. Last updated 5 years ago.

2.01 score 34 scripts 1 dependents

cran

optiSolve:Linear, Quadratic, and Rational Optimization

Solver for linear, quadratic, and rational programs with linear, quadratic, and rational constraints. A unified interface to different R packages is provided. Optimization problems are transformed into equivalent formulations and solved by the respective package. For example, quadratic programming problems with linear, quadratic and rational constraints can be solved by augmented Lagrangian minimization using package 'alabama', or by sequential quadratic programming using solver 'slsqp'. Alternatively, they can be reformulated as optimization problems with second order cone constraints and solved with package 'cccp'.

Maintained by Robin Wellmann. Last updated 3 years ago.

1.48 score 1 dependents

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