Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.

Adds smoothing spline modelling capability to nlme. Fits
        smoothing spline terms in Gaussian linear and nonlinear
        mixed-effects models.

Latent Markov models for longitudinal continuous and categorical data. See Bartolucci, Pandolfi, Pennoni (2017)<doi:10.18637/jss.v081.i04>.

Provides methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.

Introduces in-sample, out-of-sample, pseudo out-of-sample, and
    benchmark linear model forecast tests and a new class for working with 
    forecast data: Forecast.

A collection of helper functions for multiple
  regression models fitted by lm(). Most of them are simple
  functions for simple tasks which can be done with coding,
  but may not be easy for occasional users of R. Most of
  the tasks addressed are those sometimes needed when
  using the 'manymome' package (Cheung and Cheung, 2023,
  <doi:10.3758/s13428-023-02224-z>) and 'stdmod' package
  (Cheung, Cheung, Lau, Hui, and Vong, 2022,
  <doi:10.1037/hea0001188>). However, they can also be used
  in other scenarios.