Please don’t mind this post. I use this to try out various highlighting styles for my code and formats.
\begin{algorithm}
\caption{Quicksort}
\begin{algorithmic}
\PROCEDURE{Quicksort}{$A, p, r$}
\IF{$p < r$}
\STATE $q = $ \CALL{Partition}{$A, p, r$}
\STATE \CALL{Quicksort}{$A, p, q - 1$}
\STATE \CALL{Quicksort}{$A, q + 1, r$}
\ENDIF
\ENDPROCEDURE
\PROCEDURE{Partition}{$A, p, r$}
\STATE $x = A[r]$
\STATE $i = p - 1$
\FOR{$j = p$ \TO $r - 1$}
\IF{$A[j] < x$}
\STATE $i = i + 1$
\STATE exchange
$A[i]$ with $A[j]$
\ENDIF
\STATE exchange $A[i]$ with $A[r]$
\ENDFOR
\ENDPROCEDURE
\end{algorithmic}
\end{algorithm}
This would render to
\begin{algorithm}
\caption{Quicksor1t}
\begin{algorithmic}
\PROCEDURE{Quicksort}{$y_{obs}, X_{obs},n_{1},(y_{obs}, X_{obs})$}
\STATE Calculate cross-product matrix $S = X_{obs}$
\STATE \CALL{Quicksort}{$A, p, q - 1$}
\STATE \CALL{Quicksort}{$A, q + 1, r$}
\ENDIF
\ENDPROCEDURE
\PROCEDURE{Partition}{$A, p, r$}
\STATE $x = A[r]$
\STATE $i = p - 1$
\FOR{$j = p$ \TO $r - 1$}
\IF{$A[j] < x$}
\STATE $i = i + 1$
\STATE exchange
$A[i]$ with $A[j]$
\ENDIF
\STATE exchange $A[i]$ with $A[r]$
\ENDFOR
\ENDPROCEDURE
\end{algorithmic}
\end{algorithm}
\begin{algorithm}
\caption{The original \textsf{Relief} algorithm for ranking predictor variables in classification models with two classes.}
\begin{algorithmic}
\STATE Initialize the predictor scores $S_j$ to zero;
\FOR{$i = 1\ldots m$ randomly selected training set samples ($R_i$)}
\STATE Find the nearest miss and hit in the training set;
\FOR{$j = 1\ldots p$ predictor variables}
\STATE Adjust the score for each predictor based on the proximity of $R_j$ to the nearest miss and hit:\\
$S_j = S_j - diff_j(R_j, Hit)^2/m + diff_j(R_j, Miss)^2/m$;
\ENDFOR
\ENDFOR
\end{algorithmic}
\end{algorithm}library(katex)
c <- "Y^{\\operatorname{Post}} = \\beta_{0} + \\beta_{1}^{\\operatorname{Group}} + \\beta_{2}^{\\operatorname{Base}} + \\beta_{3}^{\\operatorname{Age}} + \\beta_{4}^{\\operatorname{Z}} + \\beta_{5}^{\\operatorname{R1}} + \\beta_{6}^{\\operatorname{R2}} + \\epsilon"
katex_html(c, displayMode = TRUE,
include_css = TRUE)\[Y^{\operatorname{Post}} = \beta_{0} + \beta_{1}^{\operatorname{Group}} + \beta_{2}^{\operatorname{Base}} + \beta_{3}^{\operatorname{Age}} + \beta_{4}^{\operatorname{Z}} + \beta_{5}^{\operatorname{R1}} + \beta_{6}^{\operatorname{R2}} + \epsilon\]
R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax
for authoring HTML, PDF, and MS Word documents. For more details on
using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
blogdown::shortcode('tweet', '852205086956818432')Anyone know of an R package for interfacing with Alexa Skills? @thosjleeper @xieyihui @drob @JennyBryan @HoloMarkeD ?
— Jeff Leek (@jtleek) April 12, 2017
blogdown::shortcode('youtube', '8SuDpRujc78')
Stan
/* Variable naming:
obs = observed
cen = (right) censored
N = number of samples
M = number of covariates
bg = established risk (or protective) factors
biom = candidate biomarkers (candidate risk factors)
tau = scale parameter
*/
// Tomi Peltola, tomi.peltola@aalto.fi
functions {
vector sqrt_vec(vector x) {
vector[dims(x)[1]] res;
for (m in 1:dims(x)[1]){
res[m] <- sqrt(x[m]);
}
return res;
}
vector hs_prior_lp(real r1_global, real r2_global,
vector r1_local, vector r2_local, real nu) {
r1_global ~ normal(0.0, 1.0);
r2_global ~ inv_gamma(0.5, 0.5);
r1_local ~ normal(0.0, 1.0);
r2_local ~ inv_gamma(0.5 * nu, 0.5 * nu);
return (r1_global * sqrt(r2_global)) * r1_local .* sqrt_vec(r2_local);
}
vector bg_prior_lp(real r_global, vector r_local) {
r_global ~ normal(0.0, 10.0);
r_local ~ inv_chi_square(1.0);
return r_global * sqrt_vec(r_local);
}
}
data {
int<lower=0> Nobs;
int<lower=0> Ncen;
int<lower=0> M_bg;
int<lower=0> M_biom;
vector[Nobs] yobs;
vector[Ncen] ycen;
matrix[Nobs, M_bg] Xobs_bg;
matrix[Ncen, M_bg] Xcen_bg;
matrix[Nobs, M_biom] Xobs_biom;
matrix[Ncen, M_biom] Xcen_biom;
real<lower=1> nu;
}
transformed data {
real<lower=0> tau_mu;
real<lower=0> tau_al;
tau_mu <- 10.0;
tau_al <- 10.0;
}
parameters {
real<lower=0> tau_s_bg_raw;
vector<lower=0>[M_bg] tau_bg_raw;
real<lower=0> tau_s1_biom_raw;
real<lower=0> tau_s2_biom_raw;
vector<lower=0>[M_biom] tau1_biom_raw;
vector<lower=0>[M_biom] tau2_biom_raw;
real alpha_raw;
vector[M_bg] beta_bg_raw;
vector[M_biom] beta_biom_raw;
real mu;
}
transformed parameters {
vector[M_biom] beta_biom;
vector[M_bg] beta_bg;
real alpha;
beta_biom <- hs_prior_lp(tau_s1_biom_raw,
tau_s2_biom_raw, tau1_biom_raw, tau2_biom_raw, nu) .* beta_biom_raw;
beta_bg <- bg_prior_lp(tau_s_bg_raw, tau_bg_raw) .* beta_bg_raw;
alpha <- exp(tau_al * alpha_raw);
}
model {
yobs ~ weibull(alpha, exp(-(mu + Xobs_bg *
beta_bg + Xobs_biom * beta_biom)/alpha));
increment_log_prob(weibull_ccdf_log(ycen, alpha,
exp(-(mu + Xcen_bg * beta_bg + Xcen_biom * beta_biom)/alpha)));
beta_biom_raw ~ normal(0.0, 1.0);
beta_bg_raw ~ normal(0.0, 1.0);
alpha_raw ~ normal(0.0, 1.0);
mu ~ normal(0.0, tau_mu);
}data {
int<lower=1> N;
int<lower=1> J;
int<lower=1> K;
int condition[N];
int idx[N];
vector[N] prob;
vector[N] prompt;
vector[3] a;
}
parameters {
simplex[3] phi;
real<lower=0> mu[J];
real<lower=0> sigma[J];
matrix[K,J] mu_idx;
matrix<lower=0>[K,J] sigma_idx;
real<lower=0> sigma_mu[J];
real<lower=0> sigma_sigma[J];
}
transformed parameters {
vector[N] y;
vector[N] alpha;
vector[N] beta;
vector[K] e_mu;
vector[K] e_sigma;
for (n in 1:N) {
y[n] = Phi((prompt[n] - mu_idx[idx[n], condition[n]]) /
sigma_idx[idx[n], condition[n]]);
alpha[n] = sigma_idx[idx[n], condition[n]] * y[n];
beta[n] = sigma_idx[idx[n], condition[n]] * (1.0 - y[n]);
}
for (k in 1:K) {
e_mu[k] = mu_idx[k,1] - mu[1];
e_sigma[k] = sigma_idx[k,1] - sigma[1];
}
}
model {
phi ~ dirichlet(a);
mu ~ normal(0,1);
sigma ~ normal(0,1);
sigma_mu ~ normal(0,1);
sigma_sigma ~ normal(0,1);
for (k in 1:K) {
for (j in 1:J) {
mu_idx[k,j] ~ normal(mu[j], sigma_mu[j]);
sigma_idx[k,j] ~ normal(sigma[j], sigma_sigma[j]);
}
}
for (n in 1:N) {
if (prob[n] == 0) {
target += bernoulli_lpmf(1 | phi[1]);
}
else if (prob[n] == 1) {
target += bernoulli_lpmf(1 | phi[2]);
}
else {
target += bernoulli_lpmf(1 | phi[3]) +
beta_lpdf(prob[n] | alpha[n], beta[n]);
}
}
}Python
from pandas import DataFrame
Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'pandas'
Detailed traceback:
File "<string>", line 1, in <module>
File "/Users/zad/Dropbox/LessLikely/renv/library/R-4.1/aarch64-apple-darwin20/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
import statsmodels.api as sm
Error in py_call_impl(callable, dots$args, dots$keywords): ModuleNotFoundError: No module named 'statsmodels'
Detailed traceback:
File "<string>", line 1, in <module>
File "/Users/zad/Dropbox/LessLikely/renv/library/R-4.1/aarch64-apple-darwin20/reticulate/python/rpytools/loader.py", line 39, in _import_hook
module = _import(
Stock_Market = {'Year': [2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2017,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016,2016],
'Month': [12, 11,10,9,8,7,6,5,4,3,2,1,12,11,10,9,8,7,6,5,4,3,2,1],
'Interest_Rate': [2.75,2.5,2.5,2.5,2.5,2.5,2.5,2.25,2.25,2.25,2,2,2,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75,1.75],
'Unemployment_Rate': [5.3,5.3,5.3,5.3,5.4,5.6,5.5,5.5,5.5,5.6,5.7,5.9,6,5.9,5.8,6.1,6.2,6.1,6.1,6.1,5.9,6.2,6.2,6.1],
'Stock_Index_Price': [1464,1394,1357,1293,1256,1254,1234,1195,1159,1167,1130,1075,1047,965,943,958,971,949,884,866,876,822,704,719]
}
df = DataFrame(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price'])
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'DataFrame' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
X = df[['Interest_Rate','Unemployment_Rate']] # here we have 2 variables for the multiple linear regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
Y = df['Stock_Index_Price']
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'df' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
X = sm.add_constant(X) # adding a constant
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'sm' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
model = sm.OLS(Y, X).fit()
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'sm' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
predictions = model.predict(X)
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'model' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
print_model = model.summary()
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'model' is not defined
Detailed traceback:
File "<string>", line 1, in <module>
print(print_model)
Error in py_call_impl(callable, dots$args, dots$keywords): NameError: name 'print_model' is not defined
Detailed traceback:
File "<string>", line 1, in <module>R
fit <- lm(mpg ~ cyl + disp, mtcars)
# show the theoretical model
equatiomatic::extract_eq(fit)Stata
sysuse auto2, clear
mfp: glm price mpg
twoway (fpfitci price mpg, estcmd(glm) fcolor(dkorange%20) alcolor(%40)) || scatter price mpg, mcolor(dkorange) scale(0.75)
graph export "mfp.png", replace
Deviance for model with all terms untransformed = 1373.079, 74 observations
Variable Model (vs.) Deviance Dev diff. P Powers (vs.)
----------------------------------------------------------------------
mpg Lin. FP2 1373.079 19.565 0.000+ 1 -2 -2
FP1 1356.927 3.413 0.182 -2
Final 1356.927 -2
Transformations of covariates:
-> gen double Impg__1 = X^-2-.2204707671 if e(sample)
(where: X = mpg/10)
Final multivariable fractional polynomial model for price
--------------------------------------------------------------------
Variable | -----Initial----- -----Final-----
| df Select Alpha Status df Powers
-------------+------------------------------------------------------
mpg | 4 1.0000 0.0500 in 2 -2
--------------------------------------------------------------------
Generalized linear models Number of obs = 74
Optimization : ML Residual df = 72
Scale parameter = 5533697
Deviance = 398426217.4 (1/df) Deviance = 5533697
Pearson = 398426217.4 (1/df) Pearson = 5533697
Variance function: V(u) = 1 [Gaussian]
Link function : g(u) = u [Identity]
AIC = 18.3909
Log likelihood = -678.4632599 BIC = 3.98e+08
------------------------------------------------------------------------------
| OIM
price | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Impg__1 | 13163.85 2013.016 6.54 0.000 9218.41 17109.29
_cons | 5538.395 289.7737 19.11 0.000 4970.449 6106.341
------------------------------------------------------------------------------
Deviance = 1356.927.
file /Users/zad/Dropbox/LessLikely/content/post/statistics/mfp.png saved as
PNG formatversion
clear
set obs 100
/**
Generate variables from a normal distribution like before
*/
generate x = rnormal(0, 1)
generate y = rnormal(0, 1)
/**
We set up our model here
*/
bayesmh y x, likelihood(normal({var})) prior({var}, normal(0, 10)) ///
prior({y:}, normal(0, 10)) rseed(1031) saving(coutput_pred, replace) mcmcsize(1000)
/**
We use the bayespredict command to make predictions from the model
*/
bayespredict (mean:@mean({_resid})) (var:@variance({_resid})), ///
rseed(1031) saving(coutput_pred, replace)
/**
Then we calculate the posterior predictive P-values
*/
bayesstats ppvalues {mean} using coutput_pred
Number of observations (_N) was 0, now 100.
Burn-in ...
Simulation ...
Model summary
------------------------------------------------------------------------------
Likelihood:
y ~ normal(xb_y,{var})
Priors:
{y:x _cons} ~ normal(0,10) (1)
{var} ~ normal(0,10)
------------------------------------------------------------------------------
(1) Parameters are elements of the linear form xb_y.
Bayesian normal regression MCMC iterations = 3,500
Random-walk Metropolis–Hastings sampling Burn-in = 2,500
MCMC sample size = 1,000
Number of obs = 100
Acceptance rate = .1758
Efficiency: min = .06502
avg = .08702
Log marginal-likelihood = -148.23414 max = .1192
------------------------------------------------------------------------------
| Equal-tailed
| Mean Std. dev. MCSE Median [95% cred. interval]
-------------+----------------------------------------------------------------
y |
x | .0755581 .1033197 .012813 .0920823 -.1139972 .2535904
_cons | .0764099 .1028003 .011731 .0768325 -.1117462 .2936906
-------------+----------------------------------------------------------------
var | .9489507 .1378202 .012621 .9255239 .7467658 1.289911
------------------------------------------------------------------------------
file coutput_pred.dta saved.
Computing predictions ...
file coutput_pred.dta saved.
file coutput_pred.ster saved.
Posterior predictive summary MCMC sample size = 1,000
-----------------------------------------------------------
T | Mean Std. dev. E(T_obs) P(T>=T_obs)
-------------+---------------------------------------------
mean | .0003456 .0937852 .0027069 .486
-----------------------------------------------------------
Note: P(T>=T_obs) close to 0 or 1 indicates lack of fit.─ Session info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.1.3 (2022-03-10)
os macOS Monterey 12.3
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2022-03-14
pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
─ Packages ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
! package * version date (UTC) lib source
P abind 1.4-5 2016-07-21 [?] CRAN (R 4.1.0)
P assertthat 0.2.1 2019-03-21 [?] CRAN (R 4.1.0)
P backports 1.4.1 2021-12-13 [?] CRAN (R 4.1.1)
P base64enc 0.1-3 2015-07-28 [?] CRAN (R 4.1.0)
P bcaboot 0.2-3 2021-05-09 [?] CRAN (R 4.1.0)
P blogdown 1.8.1 2022-02-18 [?] Github (rstudio/blogdown@2bc20aa)
P bookdown 0.24 2021-09-02 [?] CRAN (R 4.1.1)
boot 1.3-28 2021-05-03 [2] CRAN (R 4.1.3)
P brio 1.1.3 2021-11-30 [?] CRAN (R 4.1.1)
broom 0.7.12 2022-01-28 [1] CRAN (R 4.1.1)
P bslib 0.3.1 2021-10-06 [?] CRAN (R 4.1.1)
P cachem 1.0.6 2021-08-19 [?] CRAN (R 4.1.1)
P callr 3.7.0 2021-04-20 [?] CRAN (R 4.1.0)
P car 3.0-12 2021-11-06 [?] CRAN (R 4.1.1)
P carData 3.0-5 2022-01-06 [?] CRAN (R 4.1.1)
P cli 3.2.0 2022-02-14 [?] CRAN (R 4.1.2)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.3)
P colorspace 2.0-3 2022-02-21 [?] CRAN (R 4.1.1)
P concurve * 2.7.7 2020-10-12 [?] CRAN (R 4.1.0)
P crayon 1.5.0 2022-02-14 [?] CRAN (R 4.1.2)
P curl 4.3.2 2021-06-23 [?] CRAN (R 4.1.0)
P data.table 1.14.2 2021-09-27 [?] CRAN (R 4.1.1)
P DBI 1.1.2 2021-12-20 [?] CRAN (R 4.1.1)
P desc 1.4.0 2021-09-28 [?] CRAN (R 4.1.1)
P devtools 2.4.3 2021-11-30 [?] CRAN (R 4.1.1)
P digest 0.6.29 2021-12-01 [?] CRAN (R 4.1.1)
P dplyr 1.0.8 2022-02-08 [?] CRAN (R 4.1.1)
P ellipsis 0.3.2 2021-04-29 [?] CRAN (R 4.1.0)
P evaluate 0.15 2022-02-18 [?] CRAN (R 4.1.1)
P fansi 1.0.2 2022-01-14 [?] CRAN (R 4.1.1)
P fastmap 1.1.0 2021-01-25 [?] CRAN (R 4.1.0)
P flextable 0.6.10 2021-11-15 [?] CRAN (R 4.1.1)
P fs 1.5.2 2021-12-08 [?] CRAN (R 4.1.1)
P gdtools 0.2.4 2022-02-14 [?] CRAN (R 4.1.1)
generics 0.1.2 2022-01-31 [1] CRAN (R 4.1.1)
ggplot2 * 3.3.5 2021-06-25 [1] CRAN (R 4.1.1)
P ggpubr 0.4.0 2020-06-27 [?] CRAN (R 4.1.0)
P ggsignif 0.6.3 2021-09-09 [?] CRAN (R 4.1.1)
P glue 1.6.1 2022-01-22 [?] CRAN (R 4.1.2)
P gridExtra 2.3 2017-09-09 [?] CRAN (R 4.1.1)
P gtable 0.3.0 2019-03-25 [?] CRAN (R 4.1.1)
P htmltools 0.5.2 2021-08-25 [?] CRAN (R 4.1.1)
P httr 1.4.2 2020-07-20 [?] CRAN (R 4.1.0)
P inline 0.3.19 2021-05-31 [?] CRAN (R 4.1.0)
P jquerylib 0.1.4 2021-04-26 [?] CRAN (R 4.1.0)
jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.1.3)
P JuliaCall * 0.17.4 2021-05-16 [?] CRAN (R 4.1.0)
P kableExtra * 1.3.4 2021-02-20 [?] CRAN (R 4.1.1)
katex * 1.4.0 2022-02-08 [1] CRAN (R 4.1.1)
P km.ci 0.5-2 2009-08-30 [?] CRAN (R 4.1.0)
P KMsurv 0.1-5 2012-12-03 [?] CRAN (R 4.1.0)
knitr 1.37 2021-12-16 [1] CRAN (R 4.1.1)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.3)
P lifecycle 1.0.1 2021-09-24 [?] CRAN (R 4.1.1)
loo 2.4.1 2020-12-09 [2] CRAN (R 4.1.0)
magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.1.1)
P mathjaxr 1.4-0 2021-03-01 [?] CRAN (R 4.1.0)
Matrix 1.4-0 2021-12-08 [2] CRAN (R 4.1.3)
P matrixStats 0.61.0 2021-09-17 [?] CRAN (R 4.1.1)
P memoise 2.0.1 2021-11-26 [?] CRAN (R 4.1.1)
P metafor 3.0-2 2021-06-09 [?] CRAN (R 4.1.0)
P munsell 0.5.0 2018-06-12 [?] CRAN (R 4.1.0)
nlme 3.1-155 2022-01-16 [2] CRAN (R 4.1.3)
P officer 0.4.1 2021-11-14 [?] CRAN (R 4.1.1)
P pbmcapply 1.5.0 2019-07-10 [?] CRAN (R 4.1.0)
pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.1)
pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.1.1)
P pkgconfig 2.0.3 2019-09-22 [?] CRAN (R 4.1.0)
P pkgload 1.2.4 2021-11-30 [?] CRAN (R 4.1.1)
P png 0.1-7 2013-12-03 [?] CRAN (R 4.1.0)
P prettyunits 1.1.1 2020-01-24 [?] CRAN (R 4.1.0)
P processx 3.5.2 2021-04-30 [?] CRAN (R 4.1.0)
P ProfileLikelihood 1.1 2011-11-19 [?] CRAN (R 4.1.0)
P ps 1.6.0 2021-02-28 [?] CRAN (R 4.1.0)
P purrr 0.3.4 2020-04-17 [?] CRAN (R 4.1.0)
P R6 2.5.1 2021-08-19 [?] CRAN (R 4.1.1)
Rcpp * 1.0.8.2 2022-03-11 [2] CRAN (R 4.1.2)
RcppEigen * 0.3.3.9.1 2020-12-17 [2] CRAN (R 4.1.0)
RcppParallel 5.1.5 2022-01-05 [1] CRAN (R 4.1.1)
P remotes 2.4.2 2021-11-30 [?] CRAN (R 4.1.1)
P renv 0.15.4 2022-03-03 [?] CRAN (R 4.1.1)
reticulate * 1.24 2022-01-26 [1] CRAN (R 4.1.1)
rlang 1.0.1 2022-02-03 [1] CRAN (R 4.1.1)
P rmarkdown 2.11 2021-09-14 [?] CRAN (R 4.1.1)
P rprojroot 2.0.2 2020-11-15 [?] CRAN (R 4.1.0)
rstan * 2.21.3 2021-12-19 [2] CRAN (R 4.1.1)
P rstatix 0.7.0 2021-02-13 [?] CRAN (R 4.1.0)
P rstudioapi 0.13 2020-11-12 [?] CRAN (R 4.1.0)
P rvest 1.0.2 2021-10-16 [?] CRAN (R 4.1.1)
P sass 0.4.0 2021-05-12 [?] CRAN (R 4.1.0)
P scales 1.1.1 2020-05-11 [?] CRAN (R 4.1.0)
P sessioninfo 1.2.2 2021-12-06 [?] CRAN (R 4.1.1)
StanHeaders * 2.21.0-7 2020-12-17 [2] CRAN (R 4.1.2)
P Statamarkdown * 0.7.0 2022-01-25 [?] Github (hemken/Statamarkdown@a68a8b9)
P stringi 1.7.6 2021-11-29 [?] CRAN (R 4.1.1)
P stringr 1.4.0 2019-02-10 [?] CRAN (R 4.1.1)
survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.3)
P survminer 0.4.9 2021-03-09 [?] CRAN (R 4.1.1)
P survMisc 0.5.5 2018-07-05 [?] CRAN (R 4.1.0)
P svglite 2.1.0 2022-02-03 [?] CRAN (R 4.1.1)
systemfonts 1.0.4 2022-02-11 [1] CRAN (R 4.1.1)
P testthat 3.1.2 2022-01-20 [?] CRAN (R 4.1.2)
P tibble 3.1.6 2021-11-07 [?] CRAN (R 4.1.1)
tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.1)
P tidyselect 1.1.2 2022-02-21 [?] CRAN (R 4.1.1)
P usethis 2.1.5 2021-12-09 [?] CRAN (R 4.1.1)
P utf8 1.2.2 2021-07-24 [?] CRAN (R 4.1.0)
P uuid 1.0-3 2021-11-01 [?] CRAN (R 4.1.1)
V8 4.1.0 2022-02-06 [1] CRAN (R 4.1.1)
P vctrs 0.3.8 2021-04-29 [?] CRAN (R 4.1.0)
P viridisLite 0.4.0 2021-04-13 [?] CRAN (R 4.1.0)
P webshot 0.5.2 2019-11-22 [?] CRAN (R 4.1.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.1)
P xfun 0.29 2021-12-14 [?] CRAN (R 4.1.1)
P xml2 1.3.3 2021-11-30 [?] CRAN (R 4.1.1)
P xtable 1.8-4 2019-04-21 [?] CRAN (R 4.1.0)
P yaml 2.3.5 2022-02-21 [?] CRAN (R 4.1.1)
P zip 2.2.0 2021-05-31 [?] CRAN (R 4.1.0)
P zoo 1.8-9 2021-03-09 [?] CRAN (R 4.1.0)
[1] /Users/zad/Dropbox/LessLikely/renv/library/R-4.1/aarch64-apple-darwin20
[2] /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/library
P ── Loaded and on-disk path mismatch.
─ Python configuration ─────────────────────────────────────────────────────────────────────────────────────────────────────────
python: /usr/bin/python3
libpython: /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8/lib/python3.8/config-3.8-darwin/libpython3.8.dylib
pythonhome: /Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8:/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.8
version: 3.8.9 (default, Oct 26 2021, 07:25:53) [Clang 13.0.0 (clang-1300.0.29.30)]
numpy: [NOT FOUND]
NOTE: Python version was forced by RETICULATE_PYTHON
────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Formatted Bibliographies
ASA: , . (0001). 'Stan'. Less Likely. Available at https://lesslikely.com/statistics/stan/.
AMA: , . Stan. Less Likely. https://lesslikely.com/statistics/stan/. Published January 1, 0001.
APA: , . (0001, Jan 1). Stan. Retrieved from https://lesslikely.com/statistics/stan/.
MLA: , . Stan. Less Likely. https://lesslikely.com/statistics/stan/. 1 Jan. 0001.
Help support the website!
