Showing 149 of total 149 results (show query)

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

11.0 match 7 stars 9.11 score 1.3k scripts 6 dependents

blaserlab

datascience.curriculum:Data Science 2023

What the package does (one paragraph).

Maintained by Brad Blaser. Last updated 2 years ago.

8.0 match 1 stars 3.30 score 8 scripts

jienagu

forestry:Reshape Data Tree

'forestry' a series of utility functions to help with reshaping hierarchy of data tree, and reform the structure of data tree.

Maintained by Jiena McLellan. Last updated 5 years ago.

3.8 match 21 stars 5.66 score 44 scripts

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

1.6 match 15 stars 12.60 score 7.7k scripts 295 dependents

joshuaulrich

IBrokers:R API to Interactive Brokers Trader Workstation

Provides native R access to Interactive Brokers Trader Workstation API.

Maintained by Joshua M. Ulrich. Last updated 6 months ago.

2.0 match 69 stars 7.59 score 93 scripts

sammo3182

drhur:Learning R with Dr. Hu

Tutarials of R learning easily and happily.

Maintained by Yue Hu. Last updated 1 years ago.

1.8 match 18 stars 6.06 score 16 scripts

felixfan

FinCal:Time Value of Money, Time Series Analysis and Computational Finance

Package for time value of money calculation, time series analysis and computational finance.

Maintained by Felix Yanhui Fan. Last updated 8 years ago.

1.7 match 23 stars 6.02 score 203 scripts 1 dependents

linlf

altmeta:Alternative Meta-Analysis Methods

Provides alternative statistical methods for meta-analysis, including: - bivariate generalized linear mixed models for synthesizing odds ratios, relative risks, and risk differences (Chu et al., 2012 <doi:10.1177/0962280210393712>) - heterogeneity tests and measures and penalization methods that are robust to outliers (Lin et al., 2017 <doi:10.1111/biom.12543>; Wang et al., 2022 <doi:10.1002/sim.9261>); - measures, tests, and visualization tools for publication bias or small-study effects (Lin and Chu, 2018 <doi:10.1111/biom.12817>; Lin, 2019 <doi:10.1002/jrsm.1340>; Lin, 2020 <doi:10.1177/0962280220910172>; Shi et al., 2020 <doi:10.1002/jrsm.1415>); - meta-analysis of combining standardized mean differences and odds ratios (Jing et al., 2023 <doi:10.1080/10543406.2022.2105345>); - meta-analysis of diagnostic tests for synthesizing sensitivities, specificities, etc. (Reitsma et al., 2005 <doi:10.1016/j.jclinepi.2005.02.022>; Chu and Cole, 2006 <doi:10.1016/j.jclinepi.2006.06.011>); - meta-analysis methods for synthesizing proportions (Lin and Chu, 2020 <doi:10.1097/ede.0000000000001232>); - models for multivariate meta-analysis, measures of inconsistency degrees of freedom in Bayesian network meta-analysis, and predictive P-score (Lin and Chu, 2018 <doi:10.1002/jrsm.1293>; Lin, 2020 <doi:10.1080/10543406.2020.1852247>; Rosenberger et al., 2021 <doi:10.1186/s12874-021-01397-5>).

Maintained by Lifeng Lin. Last updated 6 months ago.

jagscpp

6.6 match 1.04 score 11 scripts

craig-pt

tsgc:Time Series Methods Based on Growth Curves

The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories. It is designed to model the progression of an epidemic over time while accounting for the various uncertainties inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach, using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves. The forecasts generated by the package are of value to public health officials and researchers who need to understand and predict the course of an epidemic to inform decision-making. Beyond its application in public health, the package is also a useful resource for researchers and practitioners in fields where the trajectories of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy. The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

Maintained by Craig Thamotheram. Last updated 7 months ago.

1.3 match 1 stars 4.86 score 24 scripts

pik-piam

mrvalidation:madrat data preparation for validation purposes

Package contains routines to prepare data for validation exercises.

Maintained by Benjamin Leon Bodirsky. Last updated 11 days ago.

0.6 match 4.81 score 9 scripts 1 dependents

fmmattioni

whippr:Tools for Manipulating Gas Exchange Data

Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.

Maintained by Felipe Mattioni Maturana. Last updated 8 months ago.

0.6 match 12 stars 4.26 score 5 scripts

tomaspinall

NFCP:N-Factor Commodity Pricing Through Term Structure Estimation

Commodity pricing models are (systems of) stochastic differential equations that are utilized for the valuation and hedging of commodity contingent claims (i.e. derivative products on the commodity) and other commodity related investments. Commodity pricing models that capture market dynamics are of great importance to commodity market participants in order to exercise sound investment and risk-management strategies. Parameters of commodity pricing models are estimated through maximum likelihood estimation, using available term structure futures data of a commodity. 'NFCP' (n-factor commodity pricing) provides a framework for the modeling, parameter estimation, probabilistic forecasting, option valuation and simulation of commodity prices through state space and Monte Carlo methods, risk-neutral valuation and Kalman filtering. 'NFCP' allows the commodity pricing model to consist of n correlated factors, with both random walk and mean-reverting elements. The n-factor commodity pricing model framework was first presented in the work of Cortazar and Naranjo (2006) <doi:10.1002/fut.20198>. Examples presented in 'NFCP' replicate the two-factor crude oil commodity pricing model presented in the prolific work of Schwartz and Smith (2000) <doi:10.1287/mnsc.46.7.893.12034> with the approximate term structure futures data applied within this study provided in the 'NFCP' package.

Maintained by Thomas Aspinall. Last updated 3 years ago.

0.5 match 5 stars 4.40 score 4 scripts

pik-piam

mrvalidnitrogen:madrat data preparation for validation purposes of nitrogen budgets

Package contains routines to prepare data for validation exercises.

Maintained by Benjamin Leon Bodirsky. Last updated 1 years ago.

0.6 match 2.18 score 1 scripts

pik-piam

mrfish:madrat data preparation for data connected to fish

Package contains routines to prepare data for validation exercises.

Maintained by Benjamin Leon Bodirsky. Last updated 1 years ago.

0.6 match 2.00 score 1 scripts

psolymos

KnockKnockJokes:Knock-Knock Jokes

An S4 exercise for Knock-Knock Joke lovers.

Maintained by Peter Solymos. Last updated 8 years ago.

0.6 match 1 stars 1.70 score 2 scripts