R-universe search: rms

rapporter

pander:An R 'Pandoc' Writer

Contains some functions catching all messages, 'stdout' and other useful information while evaluating R code and other helpers to return user specified text elements (like: header, paragraph, table, image, lists etc.) in 'pandoc' markdown or several type of R objects similarly automatically transformed to markdown format. Also capable of exporting/converting (the resulting) complex 'pandoc' documents to e.g. HTML, 'PDF', 'docx' or 'odt'. This latter reporting feature is supported in brew syntax or with a custom reference class with a smarty caching 'backend'.

Maintained by Gergely Daróczi. Last updated 16 days ago.

literate-programming markdown pandoc pandoc-markdown reproducible-research rmarkdown cpp

14.2 match 297 stars 16.60 score 7.6k scripts 108 dependents

cran

base.rms:Convert Regression Between Base Function and 'rms' Package

We perform linear, logistic, and cox regression using the base functions lm(), glm(), and coxph() in the R software and the 'survival' package. Likewise, we can use ols(), lrm() and cph() from the 'rms' package for the same functionality. Each of these two sets of commands has a different focus. In many cases, we need to use both sets of commands in the same situation, e.g. we need to filter the full subset model using AIC, and we need to build a visualization graph for the final model. 'base.rms' package can help you to switch between the two sets of commands easily.

Maintained by Jing Zhang. Last updated 5 years ago.

44.2 match 2.30 score

ips-lmu

emuR:Main Package of the EMU Speech Database Management System

Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

Maintained by Markus Jochim. Last updated 1 years ago.

13.7 match 24 stars 6.89 score 135 scripts 1 dependents

rix133

ormPlot:Advanced Plotting of Ordinal Regression Models

An extension to the Regression Modeling Strategies package that facilitates plotting ordinal regression model predictions together with confidence intervals for each dependent variable level. It also adds a functionality to plot the model summary as a modifiable object.

Maintained by Richard Meitern. Last updated 2 years ago.

rms

20.9 match 4.11 score 26 scripts

ngreifer

cobalt:Covariate Balance Tables and Plots

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with 'MatchIt', 'WeightIt', 'MatchThem', 'twang', 'Matching', 'optmatch', 'CBPS', 'ebal', 'cem', 'sbw', and 'designmatch' for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Maintained by Noah Greifer. Last updated 11 months ago.

causal-inference propensity-scores

5.8 match 75 stars 12.98 score 1.0k scripts 8 dependents

modeloriented

DALEX:moDel Agnostic Language for Exploration and eXplanation

Any unverified black box model is the path to failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection. DALEX package xrays any model and helps to explore and explain its behaviour. Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance. But such black-box models usually lack direct interpretability. DALEX package contains various methods that help to understand the link between input variables and model output. Implemented methods help to explore the model on the level of a single instance as well as a level of the whole dataset. All model explainers are model agnostic and can be compared across different models. DALEX package is the cornerstone for 'DrWhy.AI' universe of packages for visual model exploration. Find more details in (Biecek 2018) <https://jmlr.org/papers/v19/18-416.html>.

Maintained by Przemyslaw Biecek. Last updated 1 months ago.

black-box dalex data-science explainable-ai explainable-artificial-intelligence explainable-ml explanations explanatory-model-analysis fairness iml interpretability interpretable-machine-learning machine-learning model-visualization predictive-modeling responsible-ai responsible-ml xai

5.4 match 1.4k stars 13.40 score 876 scripts 21 dependents

gjmvanboxtel

gsignal:Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Maintained by Geert van Boxtel. Last updated 2 months ago.

signal-processing signals cpp

5.2 match 24 stars 10.03 score 133 scripts 34 dependents

corradolanera

depigner:A Utility Package to Help you Deal with "Pignas"

Pigna [_pìn'n'a_] is the Italian word for pine cone. In jargon, it is used to identify a task which is boring, banal, annoying, painful, frustrating and maybe even with a not so beautiful or rewarding result, just like the obstinate act of trying to challenge yourself in extracting pine nuts from a pine cone, provided that, in the end, you will find at least one inside it. Here you can find a backpack of functions to be used to solve small everyday problems of coding or analyzing (clinical) data, which would be normally solved using quick-and-dirty patches. You will be able to convert 'Hmisc' and 'rms' summary()es into data.frames ready to be rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers to activate essential but useful progress bars (from 'progress') into your loops or functionals. Easy setup and control Telegram's bots (from 'telegram.bot') to send messages or to divert error messages to a Telegram's chat. You also have some utilities helping you in the development of packages, like the activation of the same user interface of 'usethis' into your package, or call polite functions to ask a user to install other packages. Finally, you find a set of thematic sets of packages you may use to set up new environments quickly, installing them in a single call.

Maintained by Corrado Lanera. Last updated 2 years ago.

hmisc pigne rms telegram

11.5 match 3 stars 4.14 score 93 scripts

bioc

RBGL:An interface to the BOOST graph library

A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library.

Maintained by Bioconductor Package Maintainer. Last updated 4 months ago.

graphandnetwork network cpp

4.0 match 8.59 score 320 scripts 132 dependents

modeloriented

ingredients:Effects and Importances of Model Ingredients

Collection of tools for assessment of feature importance and feature effects. Key functions are: feature_importance() for assessment of global level feature importance, ceteris_paribus() for calculation of the what-if plots, partial_dependence() for partial dependence plots, conditional_dependence() for conditional dependence plots, accumulated_dependence() for accumulated local effects plots, aggregate_profiles() and cluster_profiles() for aggregation of ceteris paribus profiles, generic print() and plot() for better usability of selected explainers, generic plotD3() for interactive, D3 based explanations, and generic describe() for explanations in natural language. The package 'ingredients' is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.

Maintained by Przemyslaw Biecek. Last updated 2 years ago.

3.0 match 37 stars 10.38 score 83 scripts 22 dependents

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 3 days ago.

fortran

1.7 match 210 stars 17.61 score 17k scripts 750 dependents

jeromeecoac

seewave:Sound Analysis and Synthesis

Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.

Maintained by Jerome Sueur. Last updated 1 years ago.

3.3 match 18 stars 8.88 score 880 scripts 23 dependents

modeloriented

iBreakDown:Model Agnostic Instance Level Variable Attributions

Model agnostic tool for decomposition of predictions from black boxes. Supports additive attributions and attributions with interactions. The Break Down Table shows contributions of every variable to a final prediction. The Break Down Plot presents variable contributions in a concise graphical way. This package works for classification and regression models. It is an extension of the 'breakDown' package (Staniak and Biecek 2018) <doi:10.32614/RJ-2018-072>, with new and faster strategies for orderings. It supports interactions in explanations and has interactive visuals (implemented with 'D3.js' library). The methodology behind is described in the 'iBreakDown' article (Gosiewska and Biecek 2019) <arXiv:1903.11420> This package is a part of the 'DrWhy.AI' universe (Biecek 2018) <arXiv:1806.08915>.

Maintained by Przemyslaw Biecek. Last updated 1 years ago.

breakdown iml interpretability shapley xai

2.9 match 84 stars 10.07 score 56 scripts 22 dependents

david-cortes

outliertree:Explainable Outlier Detection Through Decision Tree Conditioning

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Maintained by David Cortes. Last updated 2 months ago.

anomaly-detection outlier-detection cpp openmp

3.8 match 58 stars 7.34 score 21 scripts 2 dependents

cran

MASS:Support Functions and Datasets for Venables and Ripley's MASS

Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).

Maintained by Brian Ripley. Last updated 16 days ago.

2.3 match 19 stars 10.53 score 11k dependents

markheckmann

OpenRepGrid:Tools to Analyze Repertory Grid Data

Analyze repertory grids, a qualitative-quantitative data collection technique devised by George A. Kelly in the 1950s. Today, grids are used across various domains ranging from clinical psychology to marketing. The package contains functions to quantitatively analyze and visualize repertory grid data (e.g. 'Fransella', 'Bell', & 'Bannister', 2004, ISBN: 978-0-470-09080-0). The package is part of the The package is part of the <https://openrepgrid.org/> project.

Maintained by Mark Heckmann. Last updated 14 days ago.

repertory-grid repgrid

3.4 match 19 stars 6.69 score 156 scripts

modeloriented

EIX:Explain Interactions in 'XGBoost'

Structure mining from 'XGBoost' and 'LightGBM' models. Key functionalities of this package cover: visualisation of tree-based ensembles models, identification of interactions, measuring of variable importance, measuring of interaction importance, explanation of single prediction with break down plots (based on 'xgboostExplainer' and 'iBreakDown' packages). To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>. 'EIX' is a part of the 'DrWhy.AI' universe.

Maintained by Ewelina Karbowiak. Last updated 4 years ago.

3.8 match 26 stars 5.72 score 6 scripts

sjtingle

rmsMD:Output Results from 'rms' Models for Medical Journals

This takes the output of models performed using the 'rms' package and returns a dataframe with the results. This output is in the format required by medical journals. For example for cox regression models, the hazard ratios, their 95% confidence intervals, and p values will be provided. There are additional functions for outputs when the model included restricted cubic spline (RCS) terms. Models using imputed data (eg from aregimpute()) and fitted used fit.mult.impute() can also be processed. The dataframe which is returned can easily be turned into a publication ready table with packages 'flextable' and 'officer'.

Maintained by Samuel Tingle. Last updated 9 days ago.

5.2 match 3.85 score

ctmm-initiative

ctmm:Continuous-Time Movement Modeling

Functions for identifying, fitting, and applying continuous-space, continuous-time stochastic-process movement models to animal tracking data. The package is described in Calabrese et al (2016) <doi:10.1111/2041-210X.12559>, with models and methods based on those introduced and detailed in Fleming & Calabrese et al (2014) <doi:10.1086/675504>, Fleming et al (2014) <doi:10.1111/2041-210X.12176>, Fleming et al (2015) <doi:10.1103/PhysRevE.91.032107>, Fleming et al (2015) <doi:10.1890/14-2010.1>, Fleming et al (2016) <doi:10.1890/15-1607>, Péron & Fleming et al (2016) <doi:10.1186/s40462-016-0084-7>, Fleming & Calabrese (2017) <doi:10.1111/2041-210X.12673>, Péron et al (2017) <doi:10.1002/ecm.1260>, Fleming et al (2017) <doi:10.1016/j.ecoinf.2017.04.008>, Fleming et al (2018) <doi:10.1002/eap.1704>, Winner & Noonan et al (2018) <doi:10.1111/2041-210X.13027>, Fleming et al (2019) <doi:10.1111/2041-210X.13270>, Noonan & Fleming et al (2019) <doi:10.1186/s40462-019-0177-1>, Fleming et al (2020) <doi:10.1101/2020.06.12.130195>, Noonan et al (2021) <doi:10.1111/2041-210X.13597>, Fleming et al (2022) <doi:10.1111/2041-210X.13815>, Silva et al (2022) <doi:10.1111/2041-210X.13786>, Alston & Fleming et al (2023) <doi:10.1111/2041-210X.14025>.

Maintained by Christen H. Fleming. Last updated 2 months ago.

1.8 match 49 stars 10.57 score 534 scripts 4 dependents

bioboot

bio3d:Biological Structure Analysis

Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information.

Maintained by Barry Grant. Last updated 5 months ago.

zlib cpp

2.0 match 5 stars 8.49 score 1.4k scripts 10 dependents

gillian-earthscope

IRISSeismic:Classes and Methods for Seismic Data Analysis

Provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the 'ObsPy' python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by EarthScope. <https://service.earthscope.org/>.

Maintained by Gillian Sharer. Last updated 3 months ago.

5.1 match 3.18 score 50 scripts 1 dependents

rwehrens

ptw:Parametric Time Warping

Parametric Time Warping aligns patterns, i.e. it aims to put corresponding features at the same locations. The algorithm searches for an optimal polynomial describing the warping. It is possible to align one sample to a reference, several samples to the same reference, or several samples to several references. One can choose between calculating individual warpings, or one global warping for a set of samples and one reference. Two optimization criteria are implemented: RMS (Root Mean Square error) and WCC (Weighted Cross Correlation). Both warping of peak profiles and of peak lists are supported. A vignette for the latter is contained in the inst/doc directory of the source package - the vignette source can be found on the package github site.

Maintained by Ron Wehrens. Last updated 3 years ago.

2.3 match 8 stars 6.31 score 57 scripts 10 dependents

bioc

MEDME:Modelling Experimental Data from MeDIP Enrichment

MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments

Maintained by Mattia Pelizzola. Last updated 5 months ago.

microarray cpgisland dnamethylation

3.3 match 4.30 score 2 scripts

jonathanlees

RSEIS:Seismic Time Series Analysis Tools

Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.

Maintained by Jonathan M. Lees. Last updated 6 months ago.

3.3 match 3 stars 4.27 score 262 scripts 4 dependents

nanxstats

hdnom:Benchmarking and Visualization Toolkit for Penalized Cox Models

Creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.

Maintained by Nan Xiao. Last updated 6 months ago.

benchmark high-dimensional-data linear-regression nomogram-visualization penalized-cox-models survival-analysis openblas

1.5 match 43 stars 8.07 score 68 scripts 1 dependents

tiesbos

EquiTrends:Equivalence Testing for Pre-Trends in Difference-in-Differences Designs

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are “equivalent” to those in the control group. Here, “equivalence” means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

Maintained by Ties Bos. Last updated 6 months ago.

openblas cpp

3.4 match 1 stars 3.54 score 4 scripts

bioc

MEDIPS:DNA IP-seq data analysis

MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis.

Maintained by Lukas Chavez. Last updated 5 months ago.

dnamethylation cpgisland differentialexpression sequencing chipseq preprocessing qualitycontrol visualization microarray genetics coverage genomeannotation copynumbervariation sequencematching

2.3 match 5.17 score 74 scripts

gforge

Greg:Regression Helper Functions

Methods for manipulating regression models and for describing these in a style adapted for medical journals. Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. In addition to the descriptive methods, there are functions for the robust covariance matrix provided by the 'sandwich' package, a function for adding non-linearities to a model, and a wrapper around the 'Epi' package's Lexis() functions for time-splitting a dataset when modeling non-proportional hazards in Cox regressions.

Maintained by Max Gordon. Last updated 1 years ago.

1.8 match 6 stars 6.26 score 68 scripts

idslme

IDSL.IPA:Intrinsic Peak Analysis (IPA) for HRMS Data

A multi-layered untargeted pipeline for high-throughput LC/HRMS data processing to extract signals of organic small molecules. The package performs ion pairing, peak detection, peak table alignment, retention time correction, aligned peak table gap filling, peak annotation and visualization of extracted ion chromatograms (EICs) and total ion chromatograms (TICs). The 'IDSL.IPA' package was introduced in <doi:10.1021/acs.jproteome.2c00120> .

Maintained by Dinesh Barupal. Last updated 2 years ago.

exposome feature-detection lipidomics mass-spectrometry metabolomics peak-detection peak-picking small-molecule untargeted-metabolomics

2.3 match 13 stars 4.89 score 1 scripts 4 dependents

cran

soundgen:Sound Synthesis and Acoustic Analysis

Performs parametric synthesis of sounds with harmonic and noise components such as animal vocalizations or human voice. Also offers tools for audio manipulation and acoustic analysis, including pitch tracking, spectral analysis, audio segmentation, pitch and formant shifting, etc. Includes four interactive web apps for synthesizing and annotating audio, manually correcting pitch contours, and measuring formant frequencies. Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.

Maintained by Andrey Anikin. Last updated 2 months ago.

2.3 match 1 stars 4.86 score 110 scripts 2 dependents

ips-lmu

wrassp:Interface to the 'ASSP' Library

A wrapper around Michel Scheffers's 'libassp' (<https://libassp.sourceforge.net/>). The 'libassp' (Advanced Speech Signal Processor) library aims at providing functionality for handling speech signal files in most common audio formats and for performing analyses common in phonetic science/speech science. This includes the calculation of formants, fundamental frequency, root mean square, auto correlation, a variety of spectral analyses, zero crossing rate, filtering etc. This wrapper provides R with a large subset of 'libassp's signal processing functions and provides them to the user in a (hopefully) user-friendly manner.

Maintained by Markus Jochim. Last updated 1 years ago.

1.3 match 24 stars 7.43 score 62 scripts 3 dependents

yikeshu0611

nomogramFormula:Calculate Total Points and Probabilities for Nomogram

A nomogram, which can be carried out in 'rms' package, provides a graphical explanation of a prediction process. However, it is not very easy to draw straight lines, read points and probabilities accurately. Even, it is hard for users to calculate total points and probabilities for all subjects. This package provides formula_rd() and formula_lp() functions to fit the formula of total points with raw data and linear predictors respectively by polynomial regression. Function points_cal() will help you calculate the total points. prob_cal() can be used to calculate the probabilities after lrm(), cph() or psm() regression. For more complexed condition, interaction or restricted cubic spine, TotalPoints.rms() can be used.

Maintained by Jing Zhang. Last updated 5 years ago.

3.0 match 3.13 score 15 scripts 1 dependents

animaltags

tagtools:Work with Data from High-Resolution Biologging Tags

High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

Maintained by Stacy DeRuiter. Last updated 9 months ago.

1.9 match 8 stars 4.62 score 26 scripts

ajiangsfu

csmpv:Biomarker Confirmation, Selection, Modelling, Prediction, and Validation

There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from 'stats', coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the 'glmnet' R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs 'XGBoost' for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with 'XGBoost' for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) <https://CRAN.R-project.org/package=survminer>, - Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023) <https://CRAN.R-project.org/package=rms>, - Harrell (2023) <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) <arXiv:1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>.

Maintained by Aixiang Jiang. Last updated 1 years ago.

2.2 match 3.70 score 1 scripts

cran

s2dv:A Set of Common Tools for Seasonal to Decadal Verification

The advanced version of package 's2dverification'. It is intended for 'seasonal to decadal' (s2d) climate forecast verification, but it can also be used in other kinds of forecasts or general climate analysis. This package is specially designed for the comparison between the experimental and observational datasets. The functionality of the included functions covers from data retrieval, data post-processing, skill scores against observation, to visualization. Compared to 's2dverification', 's2dv' is more compatible with the package 'startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The CDO version used in development is 1.9.8.

Maintained by Ariadna Batalla. Last updated 5 months ago.

3.3 match 1.95 score 3 dependents

cran

CSTools:Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the 'ERA4CS' project 'MEDSCOPE' and the 'H2020 S2S4E' project and includes contributions from 'ArticXchange' project founded by 'EU-PolarNet 2'. 'Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>'. 'Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>'. 'Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>'. 'Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>'. 'Straus et al. (2007) <doi:10.1175/JCLI4070.1>'. 'Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>'. 'Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>'. 'D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>'. 'Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>'. 'Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>'. 'Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>'.

Maintained by Victoria Agudetse. Last updated 1 years ago.

fortran

1.1 match 2 stars 5.32 score 62 scripts 1 dependents

federicoviv

Convolutioner:Convolution of Data

General functions for convolutions of data. Moving average, running median, and other filters are available. Bibliography regarding the functions can be found in the following text. Richard G. Brereton (2003) <ISBN:9780471489771>.

Maintained by Federico Maria Vivaldi. Last updated 4 years ago.

3.3 match 1.48 score 1 dependents

bavodc

CalibrationCurves:Calibration Performance

Plots calibration curves and computes statistics for assessing calibration performance. See De Cock Campo (2023) <doi:10.48550/arXiv.2309.08559> and Van Calster et al. (2016) <doi:10.1016/j.jclinepi.2015.12.005>.

Maintained by De Cock Bavo. Last updated 2 months ago.

1.2 match 4.03 score 53 scripts

jtagusari

shinyHugePlot:Efficient Plotting of Large-Sized Data

A tool to plot data with a large sample size using 'shiny' and 'plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.

Maintained by Junta Tagusari. Last updated 6 months ago.

1.7 match 4 stars 2.30 score 3 scripts

cran

IPEC:Root Mean Square Curvature Calculation

Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.

Maintained by Peijian Shi. Last updated 1 years ago.

2.4 match 1.48 score

cran

biosignalEMG:Tools for Electromyogram Signals (EMG) Analysis

Data processing tools to compute the rectified, integrated and the averaged EMG. Routines for automatic detection of activation phases. A routine to compute and plot the ensemble average of the EMG. An EMG signal simulator for general purposes.

Maintained by Antonio Guerrero. Last updated 7 years ago.

1.9 match 2 stars 1.81 score 32 scripts

koalaverse

sure:Surrogate Residuals for Ordinal and General Regression Models

An implementation of the surrogate approach to residuals and diagnostics for ordinal and general regression models; for details, see Liu and Zhang (2017, <doi:https://doi.org/10.1080/01621459.2017.1292915>) and Greenwell et al. (2017, <https://journal.r-project.org/archive/2018/RJ-2018-004/index.html>). These residuals can be used to construct standard residual plots for model diagnostics (e.g., residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). The package also provides an 'autoplot' function for producing standard diagnostic plots using 'ggplot2' graphics. The package currently supports cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. Support for binary regression models using the standard 'glm' function is also available.

Maintained by Brandon Greenwell. Last updated 13 days ago.

categorical-data diagnostics ordinal-regression residuals

0.5 match 9 stars 5.58 score 47 scripts 1 dependents

cran

DynNom:Visualising Statistical Models using Dynamic Nomograms

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of 'DynNom' supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.

Maintained by Amirhossein Jalali. Last updated 9 months ago.

1.0 match 4 stars 2.74 score

gillian-earthscope

IRISMustangMetrics:Statistics and Metrics for Seismic Data

Classes and functions for metrics calculation as part of the 'EarthScope MUSTANG' project. The functionality in this package builds upon the base classes of the 'IRISSeismic' package. Metrics include basic statistics as well as higher level 'health' metrics that can help identify problematic seismometers.

Maintained by Gillian Sharer. Last updated 3 months ago.

1.7 match 1 stars 1.43 score 27 scripts

cran

ggrcs:Draw Histograms and Restricted Cubic Splines (RCS)

You can use this function to easily draw a combined histogram and restricted cubic spline. The function draws the graph through 'ggplot2'. RCS fitting requires the use of the rcs() function of the 'rms' package. Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.

Maintained by Qiang LIU. Last updated 1 months ago.

0.5 match 1 stars 2.72 score