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r-longitudinal
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Contains general data structures and functions for longitudinal data with multiple variables, repeated measurements, and irregularly spaced time points. Also implements a shrinkage estimator of dynamical correlation and dynamical covariance.
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2025-04-22 |
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r-longcateda
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Methods for plotting categorical longitudinal and time-series data by mapping individuals to the vertical space (each horizontal line represents a participant), time (or repeated measures) to the horizontal space, categorical (or discrete) states as facets using color or shade, and events to points using plotting characters. Sorting individuals in the vertical space and (or) stratifying them by groups can reveal patterns in the changes over time.
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2025-04-22 |
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r-lomb
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Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain reliable p-values.
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2025-04-22 |
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r-logranka
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LogrankA provides a logrank test across unlimited groups with the possibility to input aggregated survival data.
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2025-04-22 |
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r-logofgamma
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Uses approximations to compute the natural logarithm of the Gamma function for large values.
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2025-04-22 |
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r-lognormreg
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Functions to fits simple linear regression models with log normal errors and identity link (taking the responses on the original scale). See Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
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2025-04-22 |
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r-lognorm
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The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>).
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2025-04-22 |
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r-logitnorm
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Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
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2025-04-22 |
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r-logisticrr
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Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
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2025-04-22 |
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r-logistic4p
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Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
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2025-04-22 |
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r-logicoil
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This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
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2025-04-22 |
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r-logger
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Inspired by the the 'futile.logger' R package and 'logging' Python module, this utility provides a flexible and extensible way of formatting and delivering log messages with low overhead.
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2025-04-22 |
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r-logcondiscr
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Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
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2025-04-22 |
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r-lodi
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Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. 'lodi' provides functions for imputing and pooling for this method.
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2025-04-22 |
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r-lock5withr
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Data sets and other utilities for 'Statistics: Unlocking the Power of Data' by Lock, Lock, Lock, Lock and Lock (ISBN : 978-0-470-60187-7, http://lock5stat.com/).
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2025-04-22 |
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r-lock5data
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Datasets for "Statistics: Unlocking the Power of Data" by Lock^5 Datasets for the first and second editions of the book. Older editions of revised data often have an extra 1e in the name.
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2025-04-22 |
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r-locfdr
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Computation of local false discovery rates.
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2025-04-22 |
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r-localsolver
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The package converts R data onto input and data for LocalSolver, executes optimization and exposes optimization results as R data. LocalSolver (http://www.localsolver.com/) is an optimization engine developed by Innovation24 (http://www.innovation24.fr/). It is designed to solve large-scale mixed-variable non-convex optimization problems. The localsolver package is developed and maintained by WLOG Solutions (http://www.wlogsolutions.com/en/) in collaboration with Decision Support and Analysis Division at Warsaw School of Economics (http://www.sgh.waw.pl/en/).
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2025-04-22 |
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r-localcontrolstrategy
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Especially when cross-sectional data are observational, effects of treatment selection bias and confounding are revealed by using the Nonparametric and Unsupervised "preprocessing" methods central to Local Control (LC) Strategy. The LC objective is to estimate the "effect-size distribution" that best quantifies a potentially causal relationship between a numeric y-Outcome variable and a t-Treatment variable. This t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on their pre-exposure X-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA. The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure is numeric with more than two levels. Otherwise, Treatment choice is Nested within BLOCKS, and effect-sizes are LOCAL Treatment Differences (LTDs) between within-cluster y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV) method is also provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute information for effect-size inferences ...assuming that X-Covariates influence only Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome. Finally, a "Most-Like-Me" function provides histograms of effect-size distributions to aid Doctor-Patient communications about Personalized Medicine.
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2025-04-22 |
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r-loadr
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Provides intuitive functions for loading objects into environments, encouraging less cluttered workspaces and sharing variables with large or reusable data across users and sessions. The user provides named variables which are loaded into the variable environment for later retrieval.
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2025-04-22 |
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r-lnirt
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Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports variable person speed functions (intercept, trend, quadratic), and covariates for item and person (random) parameters. Data missing-by-design can be specified. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
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2025-04-22 |
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r-lncpath
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Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.
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2025-04-22 |
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r-ln0scis
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Construct the simultaneous confidence intervals for ratios of means of Log-normal populations with zeros. It also has a Python module that do the same thing, and can be applied to multiple comparisons of parameters of any k mixture distributions. And we provide four methods, the method based on generalized pivotal quantity with order statistics and the quantity based on Wilson by Li et al. (2009) <doi:10.1016/j.spl.2009.03.004> (GPQW), and the methods based on generalized pivotal quantity with order statistics and the quantity based on Hannig (2009) <doi:10.1093/biomet/asp050> (GPQH). The other two methods are based on two-step MOVER intervals by Amany H, Abdel K (2015) <doi:10.1080/03610918.2013.767911>. We deduce Fiducial generalized pivotal two-step MOVER intervals based on Wilson quantity (FMW) and based on Hannig's quantity (FMWH). All these approach you can find in the paper of us which it has been submitted.
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2025-04-22 |
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r-lmviz
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Contains three shiny applications. Two are meant to explore linear model inference feature through simulation. The third is a game to learn interpreting diagnostic plots.
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2025-04-22 |
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r-lmridge
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Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc. REFERENCES i. Hoerl and Kennard (1970) <doi:10.2307/1267351> ii. Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006> iii. Imdadullah, Aslam, and Saima (2017) iv. Marquardt (1970) <doi:10.2307/1267205>.
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2025-04-22 |
